EP4619156A1

EP4619156A1 - Method and system for droplet manipulation

Info

Publication number: EP4619156A1
Application number: EP23805074.4A
Authority: EP
Inventors: Chengxun Liu; Abdulkadir YURT; Murali Jayapala; Ziduo Lin; Eva PASCAL; Bart VANDEKERCKHOVE; Long Nguyen; Aline VAN ACKER
Original assignee: Universiteit Gent; Vlaams Instituut voor Biotechnologie VIB; Interuniversitair Microelektronica Centrum vzw IMEC
Current assignee: Interuniversitair Microelektronica Centrum vzw IMEC
Priority date: 2022-11-15
Filing date: 2023-11-15
Publication date: 2025-09-24
Also published as: WO2024105091A1; CN120379763A; JP2025537809A

Abstract

The present invention is generally related to a method and system for droplet manipulation and more specifically to a method and system for droplet manipulation using electrowetting technology. The methods and systems herein described are of particular interest for analyzing cell to cell interactions, more particularly for screening for immune cell-mediated cell lysis of tumor cells.

Description

Method and system for droplet manipulation

Technical field

The present invention is generally related to a method and system for droplet manipulation and more specifically to a method and system for droplet manipulation using electrowetting technology. The methods and systems herein described are of particular interest for analyzing cell-cell interactions, more particularly for screening for immune cell-mediated cell lysis of tumor cells.

Background

Immunotherapy or the use of a patient's own immune system is seen as the holy grail in the fight against cancer. A variety of immunotherapy strategies have been evaluated, including stimulating the patient's own immune system to attack cancer cells or administering immune system components from an external source. For example, monoclonal antibodies designed to attack cancer cells in vivo have been administered alone or in genetically engineered constructs. CAR-T (chimeric antigen receptor T cell) therapy is a promising therapeutic approach in which genetically engineered T-cells express antibody-containing fusion proteins on their surface. Said fusion proteins target the T-cells to cancer cells and allow the T-cells to kill the cancer cells. Interestingly, CAR-T cells can become permanently engrafted in the patient's body, suggesting that a long-term immunity against specific cancers could be possible.

One of the key problems in CAR-T therapy is that the current heterogeneous, poorly defined starting CAR-T cell cultures lead often to suboptimal clinical outcome. Identifying or designing a highly potent homogenous CAR-T cell culture would be advantageous to provide the maximum benefit to the patient. Given that patients may have a very short window of time before the treatment can begin or before treatment success is required to prevent significant illness and mortality, solutions to overcome said key problem should also minimize the amount of time required for identifying and selecting patient-specific CAR-T treatments. Summary of the invention

The invention is set out in the appended set of claims.

In a first aspect, the present invention relates to a method for droplet manipulation comprising the steps of: providing at least a droplet in an electrowetting-on-dielectric chip wherein the droplet comprises at least a first and a second object. Illuminating at least part of the electrowetting-on-dielectric chip where the droplet is present. Obtaining at least one image from at least a first lens-free imaging device of at least part of the electrowetting-on-dielectric chip where the droplet is present. Adapting a manipulation of the droplet as a function of the at least one image. The method of the first aspect has several advantages. First, the lens-free imaging device can capture microscopy images in a large field-of-view (FOV) therefore a plurality of droplets with objects can be continuously and automatically monitored and manipulated in parallel. Second, the images captured by the lens-free imaging device enables adjustment and adaptation of one or more subsequent manipulation of the droplets.

According to an example embodiment, the step of providing at least a droplet in an electrowetting-on-dielectric chip wherein the droplet comprises at least a first and a second object, comprises: merging at least a first droplet comprising the first object and a second droplet comprising the second object. The method has the advantage that the images can be captured by the lens-free imaging device during droplet merging until the droplets are merged uniformly.

According to an example embodiment, the step of merging at least a first droplet comprising the first object and a second droplet comprising the second object is preceded by a step of: selecting the first and second droplets from corresponding a first and second groups of droplets wherein the first and second droplets comprise a predetermined amount of objects in each droplet, optionally one object in each droplet. The method has the advantage that only the droplets with objects of interest are selected to be further inspected. According to an example embodiment, the step of selecting the first and second droplets from corresponding a first and second groups of droplets wherein the first and second droplets comprise only one object in each droplet is preceded by a step of: generating the first and second groups of droplets. According to an example embodiment, at least one of the first and second objects is a biological cell. The method has the advantage that interaction of biological cell with another object can be taken into account for the adaptation of the manipulation of the object. According to an example embodiment, the other object is also a biological cell.

According to an example embodiment, the first object is an immune cell and the second object is a tumor cell.

According to an example embodiment, the at least one image is a hologram. It is an advantage of this example embodiment that the control signal can be adapted based on the raw data, i.e. hologram, from the lens-free imaging device. This allows rapid adaptation on the manipulation based on the hologram captured by the lens-free imaging device, as the processing speed on the hologram is not constrained by the computation kernel. According to an example embodiment, the at least one image is a reconstructed two-dimensional or reconstructed three-dimensional image. It is an advantage that the 2D or 3D image comprises further information than the hologram. The manipulation of the droplet can therefore be adapted as a function of reconstructed images.

According to an example embodiment, a plurality of images is obtained and wherein the plurality of the images comprises hologram, reconstructed two-dimensional and/or reconstructed three-dimensional image. The method has the advantage that the combination provides a balance between rapid adaptation provided by using hologram and detailed fine- tuning by using 2D or 3D image.

According to an example embodiment, the method further comprises obtaining at least one image from a second lens-free imaging device of at least part of the electrowetting-on- dielectric chip where the step of selecting the first and second droplets from corresponding a first and second groups of droplets wherein the first and second droplets comprise a predetermined amount of objects in each droplet, optionally one object in each droplet, and/or step of generating the first and second groups of droplets is operated, is/are performed. According to an example embodiment, the second lens-free imaging device has lower resolution than the first lens-free imaging device. The method provides advantageous costefficient setup. According to an example embodiment, the step of adapting a manipulation of the droplet as a function of the at least one image, comprises adapting the manipulation of the droplet as a function of at least a variable relating to at least a feature of the first and/or second object. According to an example embodiment, the characteristic is selected from morphological characteristic features and behavioral features. The method has the advantage of capturing a feature of the object in the droplet, in the image captured by the lens-free imaging device in at least one image.

According to an example embodiment, the step of adapting a manipulation of the droplet as a function of the at least one image, comprises adapting the manipulation of the droplet as a function of at least a variable relating to a change of at least a feature of the first and/or second object over a period of time interval. According to an example embodiment, the characteristic is selected from morphological characteristic features and behavioral features. The method has the advantage of capturing a temporal change of a feature of the object in the droplet, in the image captured by the lens-free imaging device in at least one image.

According to an example embodiment, the pixel dimension of the first lens-free imaging device is in the range of 0.2 to 20pm.

According to an example embodiment, the pixel dimension of the second lens-free imaging device is in the range of 0.2 to 50pm.

In a second aspect, the present invention relates to a system for droplet manipulation comprising: an electrowetting-on-dielectric chip, a droplet detection system comprising a light source and at least a first lens-free imaging device configured for obtaining at least one image comprising the droplet in the electrowetting-on-dielectric chip, a control unit configured for receiving the at least one image from the droplet detection system and for sending control signals to the electrowetting-on-dielectric chip. The system is configured for: providing at least a droplet in the electrowetting-on-dielectric chip wherein the droplet comprises at least a first and a second object, illuminating at least part of the electrowetting-on-dielectric chip where the droplet is present, obtaining at least one image from at least a first lens-free imaging device of at least part of the electrowetting-on-dielectric chip where the droplet is present and adapting at least one control signal for manipulating the droplet as a function of the at least one image. It is an advantage that a plurality of droplets are manipulated simultaneously in such system.

According to an example embodiment, at least 1,000 droplets are manipulated simultaneously using such a method and/or system. According to an example embodiment, at least 10,000 droplets are manipulated simultaneously using such a method and/or system.

Brief description of the drawings

Figure la shows a flow chart of a first example of a method for droplet manipulation.

Figure lb shows an example system for implementing the first example of a method for droplet manipulation.

Figure 2a shows an example zone for droplet interaction in an exemplar EWOD chip.

Figure 2b and 2c show illustrations of pairing droplets for droplet interaction in enlarged views in the droplet interaction zone in the exemplar EWOD chip in figure 2a.

Figure 3a shows an example zone for droplet generation, selection and storage in an exemplar EWOD chip.

Figure 3b shows illustrations of droplet generation, selection and storage in the exemplar EWOD chip in figure 3a.

Figure 4 shows an example zone for droplet generation preparation in an exemplar EWOD chip and illustrations of droplet generation preparation in the example zone.

Figure 5a shows an example zone for droplet collection after droplet interaction in an exemplar EWOD chip.

Figure 5b shows illustrations of droplet collection after droplet interaction in the exemplar EWOD chip in figure 5a. Figure 6 shows illustrations of using more than one lens-free imaging devices in an exemplar system for implementing an exemplar method for droplet manipulation.

Figure 7 is an illustration of how droplets are made from heterogenous cell suspension comprising at least one of two different types of cells.

Figure 8 is an illustration of how cells from specific types are first separately encapsulated and subsequently merged to obtain droplet comprising at least one of both types of cells.

Detailed description

In order that the present description can be more readily understood, certain terms are first defined. Additional definitions are set forth throughout the detailed description. The present invention is described with respect to particular embodiments and with reference to certain drawings, but the invention is not limited thereto but only by the claims. Any reference signs in the claims shall not be construed as limiting the scope. The drawings described are only schematic and are non-limiting. In the drawings, the size of some of the elements may be exaggerated and not drawn on scale for illustrative purposes.

The term "comprising", used in the claims, should not be interpreted as being restricted to the means listed thereafter; it does not exclude other elements or steps. It is thus to be interpreted as specifying the presence of the stated features, integers, steps, or components as referred to, but does not preclude the presence or addition of one or more other features, integers, steps or components, or groups thereof. The term "comprising" therefore covers the situation where only the stated features are present (and can therefore always be replaced by "consisting of" in order to restrict the scope to said stated features) and the situation where these features and one or more other features are present. The word "comprising" according to the invention therefore also includes as one embodiment that no further components are present. Thus, the scope of the expression "a device comprising means A and B" should not be interpreted as being limited to devices consisting only of components A and B. It means that with respect to the present invention, the only relevant components of the device are A and B. The term "a" shall be interpreted as a function word before a mass noun to denote a particular type or instance. It should not be interpreted as a function word before a singular noun referring one object.

Furthermore, "and/or" where used herein is to be taken as specific disclosure of each of the two specified features or components with or without the other. Thus, the term "and/or" as used in a phrase such as "A and/or B" herein is intended to include "A and B", "A or B", "A" (alone), and "B" (alone). Likewise, the term "and/or" as used in a phrase such as "A, B, and/or C" is intended to encompass each of the following aspects: A, B, and C; A, B, or C; A or C; A or B; B or C; A and C; A and B; B and C; A (alone); B (alone); and C (alone).

The terms first, second, third and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the invention described herein are capable of operation in other sequences than described or illustrated herein.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure is related. For example, the Concise Dictionary of Biomedicine and Molecular Biology, Juo, Pei-Show, 2nd ed., 2002, CRC Press; The Dictionary of Cell and Molecular Biology, 3rd ed., 1999, Academic Press; and the Oxford Dictionary of Biochemistry and Molecular Biology, Revised, 2000, Oxford University Press, provide one of skill with a general dictionary of many of the terms used in this disclosure. Practitioners are particularly directed to Sambrook et al., Molecular Cloning: A Laboratory Manual, 4th ed., Cold Spring Harbor Press, Plainsview, New York (2012); and Ausubel et al., current Protocols in Molecular Biology (Supplement 100), John Wiley & Sons, New York (2012), for definitions and terms of the art. The definitions provided herein should not be construed to have a scope less than understood by a person of ordinary skill in the art. Specifically, in the field of immunology, reference is made to Janeway et al., Immunobiology: the immune system in health and diseases, 4th ed., Elsevier Science Ltd/Garland Publishing, 1999. The terms specific to the field of CAR-T immunotherapy as known to the skilled person are referred to as in for instance Feins et al.: An introduction to chimeric antigen receptor (CAR) T-cell immunotherapy for human cancer; Am J Hematol. 2019;94:S3-S9.; Fesnak et al. Engineered T Cells: The Promise and Challenges of Cancer Immunotherapy, Nat Rev Cancer. 2016; 16(9): 566-581; Fesnak, et al. CAR-T cell therapies from the transfusion medicine perspective. Transfus Med Rev (2016) 30, 139-145.; Wang & Riviere, Clinical manufacturing of CAR-T cells: foundation of a promising therapy; Oncolytics (2016) 3, 16015; unless specifically defined herein otherwise.

Units, prefixes, and symbols are denoted in their Systeme International de Unites (SI) accepted form. Numeric ranges are inclusive of the numbers defining the range. Unless otherwise indicated, nucleotide sequences are written left to right in 5¹ to 3¹ orientation. Amino acid sequences are written left to right in amino to carboxy orientation. The headings provided herein are not limitations of the various aspects of the disclosure, which can be had by reference to the specification as a whole. Accordingly, the terms defined immediately below are more fully defined by reference to the specification in its entirety.

As used herein, the term "cell" is used interchangeably with the term "biological cell". Nonlimiting examples of biological cells include eukaryotic cells, plant cells, animal cells, such as mammalian cells, reptilian cells, avian cells, fish cells, or the alike, prokaryotic cells, bacterial cells, fungal cells, protozoan cells, or the alike, cells dissociated from a tissue, such as muscle, cartilage, fat, skin, liver, lung, neural tissue, and the alike, immunological cells, such as T cells, B cells, natural killer cells, macrophages, and the alike, embryos (e.g., zygotes), oocytes, ova, sperm cells, hybridomas, cultured cells, cells from a cell line, cancer cells, infected cells, transfected and/or transformed cells, reporter cells, and the alike. A mammalian cell can be, for example, from a human, a mouse, a rat, a horse, a goat, a sheep, a cow, a primate, or the like.

A colony of biological cells is "clonal" if all of the living cells in the colony that are capable of reproducing are daughter cells derived from a single parent cell. The term "clonal cells" refers to cells of the same clonal colony.

A "cancer cell" as herein disclosed is used interchangeable with a "tumor cell" or "tumour cell". Non-limiting examples of a cancer or tumor are solid tumors, liquid tumors, hematologic tumors, renal cell cancer, melanoma, breast cancer, prostate cancer, testicular cancer, bladder cancer, ovarian cancer, cervical cancer, stomach cancer, esophageal cancer, pancreatic cancer, lung cancer, neuroblastoma, glioblastoma, retinoblastoma, leukemia, myeloma, lymphoma, hepatoma, adenoma, sarcoma, carcinoma, blastoma, cancer of the colon, lung, kidney, liver, endometrium, cervix, ovary, thyroid, skin, or central nervous system. A "cancer cell-associated antigen" as used herein refers to an antigen present on the surface of a cancer cell. A cancer cell-associated antigen can be simple or complex; the antigen can be an epitope on a protein, a carbohydrate group or chain, a biological or chemical agent other than a protein or carbohydrate, or any combination thereof; the epitope may be linear or conformational. The cancer cell-associated antigen can be an antigen that uniquely identifies cancer cells (e.g., one or more particular types of cancer cells) or is upregulated on cancer cells as compared to its expression on normal cells. Typically, the cancer cell-associated antigen is present on the surface of the cancer cell, thus ensuring that it can be recognized by an antibody. The antigen can be associated with any type of cancer cell, including any type of cancer cell that can be found in a tumor known in the art or described herein. In particular, the antigen can be associated with lung cancer, breast cancer, melanoma, and the alike. As used herein, the term "associated with a cancer cell" when used in reference to an antigen, means that the antigen is produced directly by the cancer cell or results from an interaction between the cancer cell and normal cells.

As used herein, the term "cytotoxicity" or "cytotoxic effect" refers to the ability of a cytotoxic cell to disrupt the normal metabolism, function and/or structure of a target cell, in a potentially irreversible manner, and which often leads to cell death or killing of the target cell. Accordingly, a cytotoxic effect can refer to a cell death effect, a cytostatic effect and/or an antiproliferative effect of the cytotoxic cell on the target cell.

The term "cytotoxic cell" generally refers to a cell which can damage, injure or destroy a target cell, such as a cancer cell, a microorganism, or other diseased cells, such as vi rally-infected cells. Generally, the cytotoxic cell can bind to a target cell when co-cultured under suitable conditions, such as through an antibody, receptor, ligand or fragments/derivatives thereof, to form a stable complex therewith, which subsequently stimulates the cytotoxic cell to damage or destroy the target cell.

Exemplary cytotoxic cells include immune cells, such as natural killer (NK) cells, natural killer T cells, activated NK cells, neutrophils, T cells (inclusive of CD4 + and CD8 + T cells, y6T cells, NK T cells, and regulatory T cells) , granulocytes, eosinophils, basophils, B-cells, macrophages, lymphokine-activated killer (LAK) cells, cells with stem cell and/or progenitor cell properties (e.g., hematopoietic stem/progenitor cells) , embryonic stem cells (ESCs) , cord blood stem cells, induced pluripotent stem cells (iPSCs) , iPSC-derived T cells and iPSC-derived NK cells, albeit without any limitation thereto.

In particular embodiments, the cytotoxic cell is an immune cell having a receptor that binds to a target protein, such as an antigen. As generally used herein, the term "receptor" refers to a polypeptide, or portion thereof, present on a cell membrane that selectively binds one or more ligands. In some embodiments, the cytotoxic cell is additionally or alternatively capable of exerting a cytotoxic effect on a target cell without having a receptor binding to a target protein or ligand thereon (e.g., macrophagocytosis).

The term "immune cells" as used herein refers to cells that are part of the immune system (which can be either the adaptive or the innate immune system). Immune cells as used herein are typically immune cells that are manufactured for adoptive cell transfer (either autologous transfer or allogeneic transfer). Many different types of immune cells are used for adoptive therapy and thus are envisaged for use in the methods described herein. Examples of immune cells include, but are not limited to T cells, NK cells, NKT cells, lymphocytes, dendritic cells, myeloid cells, stem cells, progenitor cells or iPSCs. The latter three are not immune cells as such, but can be used in adoptive cell transfer for immunotherapy (see e.g. Jiang et al. 2014 Cell Mol Immunol; Themeli et al. 2015 Cell Stem Cell). Typically, while the manufacturing starts with stem cells or iPSCs (or may even start with a dedifferentiation step from immune cells towards iPSCs), manufacturing will entail a step of differentiation to immune cells prior to administration. Stem cells, progenitor cells and iPSCs used in manufacturing of immune cells for adoptive transfer (i.e., stem cells, progenitor cells and iPSCs or their differentiated progeny that are transduced with a chimeric antigen receptor or CAR as described herein) are considered as immune cells herein. According to particular embodiments, the stem cells envisaged in the methods do not involve a step of destruction of a human embryo.

Particularly envisaged immune cells include white blood cells (leukocytes), including lymphocytes, monocytes, macrophages and dendritic cells. Particularly envisaged lymphocytes include T cells, NK cells and B cells, most particularly envisaged are T cells. In the context of adoptive transfer, note that immune cells will typically be primary cells (i.e. cells isolated directly from human or animal tissue, and not or only briefly cultured), and not cell lines (i.e. cells that have been continually passaged over a long period of time and have acquired homogenous genotypic and phenotypic characteristics). According to specific embodiments, the immune cell is a primary cell. According to alternative specific embodiments, the immune cell is not a cell from a cell line.

In particular embodiments, the immune cells is a T cell or more particularly a CAR-T cell.

A "chimeric antigen receptor" or "CAR" as used herein refers to a chimeric receptor (i.e. an artificially constructed hybrid protein or polypeptide composed of parts from different sources) that has at least a binding moiety with a specificity for an antigen (which can e.g. be derived from an antibody, a receptor or its cognate ligand) and a signaling moiety that can transmit a signal in an immune cell (e.g. a CD3 zeta chain). Characteristics of CARs include their ability to redirect T-cell specificity and reactivity toward a selected target in a non-MHC-restricted manner and exploiting the antigen-binding properties of monoclonal antibodies. The non- MHC-restricted antigen recognition gives T cells expressing CARs the ability to recognize an antigen independent of antigen processing, thus bypassing a major mechanism of tumor escape. Moreover, when expressed in T-cells, CARs advantageously do not dimerize with endogenous T cell receptor (TCR) alpha and beta chains.

In particular embodiments, the CAR described herein comprises an antigen binding domain of a monoclonal antibody known in the art. To this end, the antigen binding domain may comprise a light chain variable region and/or a heavy chain variable region of a monoclonal antibody.

In other particular embodiments, the CAR described herein comprises an antigen binding domain of a immunoglobulin single variable domain of ISVD known in the art. The term "ISVD" defines molecules wherein the antigen binding site is present on, and formed by, a single immunoglobulin domain. Hence, the binding site of an ISVD is formed by a single VH/VHH or VL domain and consists of no more than three CDRs, in contrast to a total of 6 CDRs that are involved in antigen binding site formation of "conventional" immunoglobulins. A non-limiting example of ISVDs are "VHH domains", also known as VHHs, VHH domains, VHH antibody fragments, VHH antibodies or Nanobodies®. VHHs have originally been described as the antigen-binding immunoglobulin (Ig) (variable) domain of "heavy chain antibodies" (i.e., of "antibodies devoid of light chains"; Hamers-Casterman et al (1993) Nature 363: 446-448). The VHH may be derived from, for example, an organism that produces VHH antibodies such as a camelid, a shark (also called VNAR), or the VHH may be a designed VHH. For a further description of VHHs and Nanobody®, reference is made to the review article by Muyldermans (Reviews in Molecular Biotechnology 74: 277-302, 2001), as well as to the following patent applications, which are mentioned as general background art: WO 94/04678, WO 95/04079 and WO 96/34103 of the Vrije Universiteit Brussel; WO 94/25591, WO 99/37681, WO 00/40968, WO 00/43507, WO 00/65057, WO 01/40310, WO 01/44301, EP 1134231 and WO 02/48193 of Unilever; WO 97/49805, WO 01/21817, WO 03/035694, WO 03/054016 and WO 03/055527 of the Vlaams Instituut voor Biotechnologie (VIB); WO 03/050531 of Algonomics N.V. and Ablynx N.V.; WO 01/90190 by the National Research Council of Canada; WO 03/025020 (= EP 1433793) by the Institute of Antibodies; as well as WO 04/041867, WO 04/041862, WO 04/041865, WO 04/041863, WO 04/062551, WO 05/044858, WO 06/40153, WO 06/079372, WO 06/122786, WO 06/122787 and WO 06/122825, by Ablynx N.V. and the further published patent applications by Ablynx N.V. The CAR described herein may thus comprises an antigen binding domain of an ISVD or more particularly a VHH known in the art.

"T-cell receptor" as used herein refers to a molecule capable of recognizing a peptide when presented by the major histocompatibility complex of MHC.

"Antibody" refers to all isotypes of immunoglobulins (IgG, IgA, IgE, IgM, IgD, and IgY) including various monomeric, polymeric and chimeric forms, unless otherwise specified. Specifically encompassed by the term "antibody" are polyclonal antibodies, monoclonal antibodies (mAbs), and antibody-like polypeptides, such as chimeric antibodies and humanized antibodies. "Antigen-binding fragments" are any proteinaceous structure that may exhibit binding affinity for a particular antigen. Antigen-binding fragments include those provided by any known technique, such as enzymatic cleavage, peptide synthesis, and recombinant techniques. Some antigen-binding fragments are composed of portions of intact antibodies that retain antigenbinding specificity of the parent antibody molecule. For example, antigen-binding fragments may comprise at least one variable region (either a heavy chain or light chain variable region) or one or more CD Rs of an antibody known to bind a particular antigen. Examples of suitable antigen-binding fragments include, without limitation single-chain molecules as well as Fab, F(ab')2, Fc, Fabc, and Fv molecules, single chain antibodies, individual antibody light chains, individual antibody heavy chains, chimeric fusions between antibody chains or CDRs and other proteins, protein scaffolds, heavy chain monomers or dimers, light chain monomers or dimers, dimers consisting of one heavy and one light chain, a monovalent fragment consisting of the VL, VH, CL and CHI domains, bivalent fragments comprising two Fab fragments linked by a disulfide bridge at the hinge region, a Fv fragment consisting essentially of the VL and VH domains of a single arm of an antibody, a dAb fragment (Ward et al. 1989 Nature 341) which consists essentially of a VH domain and also called domain antibodies (Holt et al 2003 Trends Biotechnol 21); camelid or nanobodies (Revets et al 2005 Expert Opin Biol Ther 5); an isolated complementarity determining region (CDR), and the alike. All antibody isotypes may be used to produce antigen-binding fragments. Additionally, antigen-binding fragments may include non-antibody proteinaceous frameworks that may successfully incorporate polypeptide segments in an orientation that confers affinity for a given antigen of interest, such as protein scaffolds. Antigen-binding fragments may be recombinantly produced or produced by enzymatic or chemical cleavage of intact antibodies. The phrase "an antibody or antigenbinding fragment thereof" may be used to denote that a given antigen binding fragment incorporates one or more amino acid segments of the antibody referred to in the phrase.

The term "epitope" means a protein determinant capable of specific binding to an antibody. Epitopes usually consist of surface groupings of molecules such as amino acids or sugar side chains and usually have specific three-dimensional structural characteristics, as well as specific charge characteristics. Conformational and non-conformational epitopes are distinguished in that the binding to the former but not the latter is lost in the presence of denaturing solvents. The epitope may comprise amino acid residues directly involved in the binding and other amino acid residues, which are not directly involved in the binding, such as amino acid residues which are effectively blocked or covered by the specifically antigen binding peptide (in other words, the amino acid residue is within the footprint of the specifically antigen binding peptide).

"Specific binding" or "immune-specific binding" or derivatives thereof when used in the context of antibodies, or antibody fragments, represents binding via domains encoded by immunoglobulin genes or fragments of immunoglobulin genes to one or more epitopes of a protein of interest, without preferentially binding other molecules in a sample containing a mixed population of molecules.

As used herein, by "isolated" is meant material, such as a cytotoxic cell or a target cell, that has been removed from its natural state or otherwise been subjected to human manipulation. Isolated material may be substantially or essentially free from components that normally accompany it in its natural state, or may be manipulated so as to be in an artificial state together with components that normally accompany it in its natural state. Isolated material may be in recombinant, chemical synthetic, enriched, purified or partially purified form. A "clone" is a population of cells derived from a single cell or common ancestor by mitosis.

A "cell line" is a clone of a primary cell that is capable of stable growth in vitro for many generations. In some examples provided herein, cells are transformed by transfecting the cells with DNA.

The term "determining" includes any form of measurement, and includes determining if an element is present or not. As used herein, the terms "determining", "measuring", "evaluating", "assessing" and "assaying" are used interchangeably and include quantitative and qualitative determinations. Determining may be relative or absolute. "Determining the presence of" includes determining the amount of something present (e.g., a cytotoxic effect, such as those described herein), and/or determining whether it is present or absent.

The terms "express" and "produce" are used synonymously herein, and refer to the biosynthesis of a gene product. These terms encompass the transcription of a gene into RNA. These terms also encompass translation of RNA into one or more polypeptides, and further encompass all naturally occurring post-transcriptional and post-translational modifications.

"Electrowetting-on-Dielectric" or "EWOD" is one of the most popular digital microfluidic techniques, deployed in Lab-on-a-Chip (LoC) systems for analyzing samples of biomedical relevance. Triggered by an electrical stimulus, EWOD devices allow precise manipulation of single droplets along the designed electrode arrays without employing external pumps and valves. A typical EWOD system is comprised of lithography patterned electrodes on a substrate enveloped with a thin dielectric and hydrophobic top layer (Shen et al 2014 Microfluid Nanofluidics 16; Vergauwe et al 2011 Microfluid Nanofluidics 11; Samad et al 2015 Procedia Technol 20). Usually, a liquid droplet is placed in the intermediate position by the electrode embedded top plate covered with a hydrophobic layer, to which a DC (direct current) or AC (alternating current) voltage is applied (Yang et al 2016 Lab Chip 16; Nelson and Kim 2012 J Adhes Sci Technol; Lin and Yao 2012 J Adhes Sci Technol). This leads to the generation of an electric field between the top electrode and the liquid droplet, changing the wettability at the dielectric/droplet interface and attracting the droplet to the surface (Nelson and Kim 2012 J Adhes Sci Technol; Lin and Yao 2012 J Adhes Sci Technol). "Lens-free imaging" or "LFI" involves inserting a sample between a light source and a matrix photodetector, or image sensor. The image collected on the photodetector is formed by interference between the incident wave produced by the light source and the wave diffracted by the particles making up the sample. This image is frequently referred to as "hologram". It is then possible to analyse each particle, comparing the diffraction pattern that it generates with figures of diffraction previously established, corresponding to known particles.

The disclosure will be further elucidated by means of the following description and the appended figures. Various exemplary embodiments are described herein with reference to the following figures, wherein like numeral denotes like entities. The figures described are schematic and are non-limiting. Further, any reference signs in the claims shall not be construed as limiting the scope of the present disclosure. Still further, in the different figures, the same reference signs refer to the same or analogous elements.

According to a first aspect, the present invention relates to a method for droplet manipulation as shown in figure la. The method comprises the steps of: a) providing at least a droplet in an electrowetting-on-dielectric chip wherein the droplet comprises at least a first and a second object; b) illuminating at least part of the electrowetting-on-dielectric chip where the droplet is present; c) obtaining at least one image from at least a first lens-free imaging device of at least part of the electrowetting-on-dielectric chip where the droplet is present, d) adapting a manipulation of the droplet as a function of the at least one image.

A droplet refers to a small volume, for example in a range of 30 pL, i.e. picoliter, to 100 pL, i.e. microliter, of a first fluid in a second fluid where the first and the second fluids are immiscible.

In a particular embodiment of the current application, a droplet is a microdroplet. In a further particular embodiment, the microdroplet is a nanoliter droplet. In another further particular embodiment, the microdroplet is a picoliter droplet.

According to an example embodiment, the first fluid is aqueous and the second fluid is oil based, such as silicone oil. According to an example embodiment, the droplet can further comprise particles and/or objects, such as a biological cell, air bubbles, another droplet, etc. Droplet manipulation generally refers to actions applied to droplets, including and not limited to: changing the location of the droplet(s), droplet movement, interacting droplets, mixing, merging, splitting and/or causing a change of a property of the droplet(s).

According to an example embodiment, the droplet comprises a plurality of objects. According to an example embodiment, the droplet comprises a first and second object. According to an example embodiment, the object can be, but not limited to molecules such as magnetic beads or a biological entity such as cell, molecule, molecule fragment, etc. The biological cell refers to a structural and functional unit of life form such as a cell formed with a cytoplasm enclosed within a membrane, which contains many biomolecules such as proteins and nucleic acids. The molecule refers to biological molecules such as RNA, DNA, protein, etc. The molecule fragment can be RNA fragment, DNA fragment, peptide, etc.

An example EWOD chip (102), as shown in figure lb, comprises an EWOD layer (25) a transparent cover (24), and a thin-film transistor (TFT) backplane (26). The EWOD layer (25) and the transparent cover (24) leave a distance therebetween, creating an operation space where the droplets are manipulated and the fluid around droplets is placed. The operation space can be referred as an electrowetting layer. The operation space usually has a height of lO-lOOOpm to allow droplet manipulation. According to an example embodiment, the transparent cover (24) is a glass cover. According to an example embodiment, the EWOD chip further comprises fluidics such as reservoirs to provide the droplets and the fluids. According to an example embodiment, the reservoirs contain bio-chemical materials such as cell medium, buffers, oils, water, etc. According to an example embodiment, the bio-chemical materials can be supplied by an attached fluidic pump to the reservoirs or can be supplied via pipettes, manually or automatically. Such bio-chemical materials can be contained in tubes, containers, plates, syringes which, for example, are attached to the fluidic pump.

According to an example embodiment, at least part of the EWOD chip is illuminated. According to an example embodiment, at least 80% of the EWOD chip is illuminated. According to an example embodiment, at least one light source (31) is used for emitting light and illuminating the EWOD chip, as shown in an exemplar system in figure lb. According to an example embodiment, a single light source is used for emitting light and illuminating the EWOD chip at multiple and distinct wavelengths. According to an example embodiment, a plurality of light sources are used for emitting light and illuminating the EWOD chip. According to an example embodiment, the light source comprises multiple sub light sources emitting light and illuminating the droplets at unique and distinct wavelengths.

The lens-free imaging device (32), as shown in an exemplar system in figure lb, captures hologram, where the droplet is present, from the interference of the light. The at least one image is a hologram, reconstructed two-dimensional (2D) or reconstructed three-dimensional (3D) image from the hologram.

According to an example embodiment, the image is a hologram image. Features captured in a raw hologram, such as the location, size, shape, refractive index, turbidity, etc, of the droplet and/or the object in the droplet can be used directly as variables for adapting the manipulation of the droplet. These features can be found through a supervised model or analytical model developed on the holograms. This allows fast adaptation of the manipulation because the speed of processing is not constrained by the computation kernel which is required by the reconstruction process.

According to an example embodiment, the image is a reconstructed image from the hologram. The reconstruction processing entails numeric calculations on the two-dimensional images obtained from the image sensor. Such numeric calculations include, for instance, deconvolutions, the wave or beam propagation, transformations (such as Fourier Transforms), two-dimensional filtering operations such as denoising, etc. The reconstructed image reveals physical features of the droplet.

According to an example embodiment, a plurality of images are obtained. The plurality of the images comprises a combination of hologram, 2D and/or reconstructed 3D image.

According to an example embodiment, the image can be processed as a whole. According to an example embodiment, the image can be divided into sub areas and the sub areas are processed individually. The processing can be therefore performed in parallel for different sub areas.

As default, a manipulation of the droplet is designed. According to an example embodiment, the detection of a feature or a change of a feature from the image can be used as a triggering event to adapt the default manipulation. According to an example embodiment, such adaptation is to send the droplet to a location other than a predetermined location in the default manipulation. According to an example embodiment, such adaptation is to change a contact angle of the droplet.

According to an example embodiment, the feature is a morphological characteristic feature. Morphological characteristic features include but not limited to: the object size & shape (such as circularity), size & shape of a sub-unit of the object; the spectral absorbance or transmittance of the object, etc.

According to an example embodiment, the feature is a behavioral feature of one object. Such behavioral features include but are not limited to: the object's migration speed, swimming speed, rotational speed, and frequency of shape change of the object, i.e. from circular to elongated shape and back to circular, etc. According to an example embodiment, the feature is a behavioral feature from interaction of more than one object. Such behavioral features include but not limited to: object to object interaction frequency and temporal length of the interaction, etc.

According to an example embodiment, the manipulation of the droplet is adapted as a function of one variable or a combination of a plurality of variables relating to one aforementioned feature of one object. According to an example embodiment, the manipulation of the droplet is adapted as a function of a combination of a plurality of variables relating to a plurality of different aforementioned feature of one object. According to an example embodiment, the manipulation of the droplet is adapted as a function of a combination of a plurality of variables relating to a plurality of different aforementioned feature of more than one object. According to an example embodiment, the plurality of different aforementioned feature can be morphological characteristic features and/or behavioral features.

According to an example embodiment, the manipulation of the droplet is adapted as a function of at least a variable relating to at least an aforementioned feature of the first and/or second object. It means, in an example, the appearance of an aforementioned feature as predetermined by the user in operation is the triggering event for the adaptation. It can also mean, in an example, the missing of an aforementioned feature as predetermined by the user in operation is the triggering event for the adaptation. According to an example embodiment, the manipulation of the droplet is adapted as a function of at least a variable relating to a change of the at least an aforementioned feature of the first and/or second object over a period of time interval. It means, in an example, the temporal change of an aforementioned feature as predetermined by the user in operation is the triggering event for the adaptation. It can also mean, in an example, the missing of a temporal change of an aforementioned feature as predetermined by the user in operation is the triggering event for the adaptation.

According to an example embodiment, at least one of the first and second objects is a biological cell. According to an example embodiment, the first object is a biological cell and the second object is a labelling molecule such as a magnetic bead. According to an example embodiment, both first and second objects are biological cells.

According to an example embodiment, the feature is a morphological characteristic feature. Morphological characteristic features include but not limited to: the cell size & shape (such as circularity), cell nucleus size & shape, the nucleus/cell size ratio; the spectral absorbance or transmittance of the cytoplasm, cell nucleus or the cell membrane and the ratio of the absorbance or transmittance between the cytoplasm, cell nucleus and cell membrane, etc.

According to an example embodiment, the feature is a behavioral feature of one cell. Such behavioral features include but not limited to: the cell migration speed, swimming speed, rotational speed, and frequency of shape change of the cell, i.e. from circular to elongated shape and back to circular, etc. According to an example embodiment, the feature is a behavioral feature from interaction of more than one cell. Such behavioral features include but not limited to: cell to cell interaction frequency and temporal length of the interaction, etc.

According to an example embodiment, the manipulation of the droplet is adapted as a function of one variable or a combination of a plurality of variables relating to one aforementioned feature of one cell. According to an example embodiment, the manipulation of the droplet is adapted as a function of a combination of a plurality of variables relating to a plurality of different aforementioned feature of one cell. According to an example embodiment, the manipulation of the droplet is adapted as a function of a combination of a plurality of variables relating to a plurality of different aforementioned feature of more than one cell. According to an example embodiment, the plurality of different aforementioned feature can be morphological characteristic features and/or behavioral features.

According to an example embodiment, the manipulation of the droplet is adapted as a function of at least a variable relating to at least an aforementioned feature of the first and/or second cell. It means, in an example, the appearance of an aforementioned feature as predetermined by the user in operation is the triggering event for the adaptation. It can also mean, in an example, the missing of an aforementioned feature as predetermined by the user in operation is the triggering event for the adaptation.

According to an example embodiment, the manipulation of the droplet is adapted as a function of at least a variable relating to a change of the at least an aforementioned feature of the first and/or second cell over a period of time interval. It means, in an example, the temporal change of an aforementioned feature as predetermined by the user in operation is the triggering event for the adaptation. It can also mean, in an example, the missing of a temporal change of an aforementioned feature as predetermined by the user in operation is the triggering event for the adaptation.

According to an example embodiment, the first object is an immune cell and the second object is a tumor cell. Non-limiting examples of an immune cell are (as described in the definitions section) T-cells, natural killers (NK) cells, macrophages, dendritic cells (DC), B cells and any i PSC- derived cell type. In a particular embodiment, the immune cell is a T-cell. In an even more particular embodiment, the immune cell is a CAR-T cell.

According to an example embodiment, the step (a) of providing at least a droplet in an electrowetting-on-dielectric chip wherein the droplet comprises at least a first and a second object, comprises a step (a.l) merging at least a first droplet comprising the first object and a second droplet comprising the second object.

According to an example embodiment, as shown in figure 2a, the merging of droplets happens in a central area of the EWOD chip.

The central area can comprise a zone for droplet interaction and zones for droplet storage as shown in figure 2b. According to an example embodiment, rows of droplets comprising the first object are stored in the first droplet storage zone and rows of droplets comprising the second object are stored in the second droplet storage zone. The row of droplets is controlled by the corresponding row of electrodes having contact with the droplets. The rows of droplets in the droplet storage zone have at least one row of electrodes where no droplets are located therebetween. According to an example embodiment, the row of droplets comprising the first object is in the neighboring row of the row of droplets comprising the second object. According to an example embodiment, as shown in figure 2c, the rows of the droplets comprising the first or second object are displaced as such that the droplets comprising the first object are in same column as the droplets comprising the second object in a droplet interaction zone. The droplets comprising the first or second object are in the neighboring row, the two neighboring droplets forms object couple. The object couple will be merged into one droplet which comprises at least the first and second objects. According to an example embodiment, the columns of droplets are separated by at least one column of electrodes where no droplets with objects are located therebetween. These columns can be used for transporting reagent and product produced from the objects in the droplets.

According to an example embodiment, the step (a.l) of merging at least a first droplet comprising the first object and a second droplet comprising the second object is preceded by a step of: z. selecting the first and second droplets from corresponding a first and second groups of droplets wherein the first and second droplets comprise a predetermined amount or number of objects in each droplet, optionally one object in each droplet.

According to an example embodiment, as shown in figure 2a, the selecting of droplets happens in at least one side area on the EWOD chip.

The side area can comprise a zone for droplet generation and a zone for droplet examination and selection as shown in figure 3b. The selected droplets will be then transported to the droplet storage zone as shown in figure 3b. In the droplet generation zone for generating the droplets with first objects, a first group of droplets are generated.

After a solution containing hundreds of thousands of objects in the range of approximately 1- 100 microliters is loaded to a side area of the EWOD chip in a loading zone as shown in figure 4, the electrodes in the side area are activated to further load the solution into several columns of the electrode array as shown in figure 3b. A certain volume of the solution is thus present on the EWOD chip. More than one droplet can be generated in rows in parallel. With a realistic design of row size of 400 electrodes and a frame rate of 10 Hz, it is feasible to generate hundreds of single droplets per second from one inlet of the cartridge. In a matter of several minutes, hundreds of thousands of single droplets can be possibly created in the EWOD chip.

According to an example embodiment, the rows of droplets have at least one reserved row of electrodes where no droplets are located therebetween. During examination and selection of the droplets, the droplets having a predetermined amount or number of objects will be selected and displaced onto the reserved row of electrodes. The selected droplets will be transported to the droplet storage zone. According to an example embodiment, the unselected droplets can be moved back to the loading zone. According to an example embodiment, the predetermined amount or number of objects is one object.

According to an example embodiment, there are at least two side areas on the EWOD chip configured for droplet generation, examination and selection. According to an example embodiment, the two side areas are located on the opposite side of the EWOD chip.

According to an example embodiment, as shown in figure 5a, at least one outlet reservoir is attached to the EWOD chip in at least one side area thereof.

According to an example embodiment, as shown in figure 5b, droplets containing a certain type of object can be displaced to and collected by the outlet reservoir. According to an example embodiment, there are a plurality of outlet reservoirs attached to the EWOD chip for collecting the objects in droplets.

According to an example embodiment, the droplet detection system comprises a plurality of lens-free imaging devices. According to an example embodiment, at least a first and second lens-free imaging devices are used to take images from different areas of the EWOD chip. According to an example embodiment, the first lens-free imaging device is taking images of at least the interaction zone. The second lens-free imaging device takes images of at least the droplet generation zone. According to an example embodiment, the second lens-free imaging device has the same resolution as the first lens-free imaging device. According to another example embodiment, the second lens-free imaging device has a lower resolution than the first lens-free imaging device. According to an example embodiment, the plurality of lens-free imaging devices is operated in the same lens-free imaging module wherein at least some of the electronics and software are shared in the module. It is an advantage that different imaging requirements of the different lens-free imaging devices can be met with minimal modifications in terms of hardware and software.

The different stages of the droplet manipulation process, such as droplet generation, selection, inspection, interaction etc. in the corresponding zones on EWOD, require different performance specifications from the imaging device.

According to an example embodiment, the droplet generation process occurs very rapidly, for example less than 1 sec. According to an example embodiment, the process occurs between 0.5 to 1 sec. The generation zone is in a side area of the EWOD device which is less than 100 mm².

According to an example embodiment, the droplet inspection process requires longer imaging time and resolution, for example, larger than 1 sec in a larger area of at least 1000mm². According to an example embodiment, the process requires between 5 to 10 sec.

According to an example embodiment, the droplet interaction process requires longer imaging time and resolution, for example, larger than 10 sec in a larger area of at least 1000mm². According to an example embodiment, the process requires between 30 to 60 sec.

According to an example embodiment, a different lens-free imaging device is used for a different process. According to an example embodiment, a different lens-free imaging device is used for more than one process. According to an example embodiment, the plurality of lens- free imaging devices is operated in the same lens-free imaging module wherein at least some of the electronics and software are shared in the module. It is an advantage that different imaging requirements of the different lens-free imaging devices for different process steps can be met with minimal modifications in terms of hardware and software. It is also an advantage that the lens-free imaging devices and module can be designed concisely, cost effective and more robust regarding the mechanical issues during operation and setup. According to an example embodiment, as shown in figure 6, the first scenario, the droplet detection system comprises a first, second, third, fourth and fifth lens-free imaging devices. The pixel dimension of the first, fourth and fifth lens-free imaging device is in the range of 1- 100 micrometers whereas the second and third lens-free imaging device has a pixel size of 0.2- 1 micrometer. According to the same example embodiment the pixel array size of the first, fourth and fifth lens-free imaging device can be smaller or larger than the second lens-free imaging devices. According to the same example embodiment the pixel array size of the first, fourth and fifth lens-free imaging device is larger than 100mm². Moreover, lens-free imaging devices can be enumerated meaning that they can be arrayed symmetrically or asymmetrically with respect to a central axis of the surface of EWOD chip. During the different process steps of the application, one or more lens-free imaging devices can be activated to obtain images of the droplets and the objects in the droplets in parallel. The image-based analysis be used for adapting the manipulation of the droplets.

According to an example embodiment, the light source 32 comprises a plurality of submodules in a matrix. According to an example embodiment, as shown in figure 6, the second scenario, the droplet detection system comprises a sixth and seventh lens-free imaging devices which have the same pixel size. The different optical resolution requirements can be met through adapting the light source, i.e. multiplexing the submodules in such a way that smaller virtual pixels are obtained. Such light source 32 comprises a matrix of at least n x m where n, m >2. The distance between the submodules can be 50 micron - 10 mm. Different arrays can be paired with different lens-free imaging devices so that spatial resolution specifications can be adapted based on the requirements of different process steps of the droplets. It is important to note the trade-off between the spatial and time resolution in this scenario. As the larger the number of submodules, longer would it take to create a higher resolution image since each submodules illuminates the droplet in sequence.

According to an example embodiment, at least one of the lens-free imaging devices is mounted on a unidirectional mechanical scanning stage where images are obtained by the lens-free imaging device in one axis in the FOV. According to an example embodiment, at least one of the lens-free imaging devices is mounted on a bi-directional mechanical scanning stage where images are obtained by the lens-free imaging device in two orthogonal axes in the FOV. According to an example embodiment, the pixel dimension of the first lens-free imaging device is in the range of 0.2 to 20pm. According to an example embodiment, the pixel dimension of the second lens-free imaging device is in the range of 0.2 to 50pm. According to an example embodiment, the second lens-free imaging device has lower resolution than the first lens-free imaging device.

According to an example embodiment, the pixel dimension of the second lens-free imaging device is in the range of 1 to 50pm.

In a second aspect, the present invention is related to a system (100) or device as shown in figure lb comprising an EWOD chip (102), a droplet detection system (103), a control unit (104). The droplet detection system (103) comprises a lens-free imaging device (31). The term "lens- free imaging device" can be interchangeable with "lens-free holographic imaging device". According to an example embodiment, the droplet detection system (103) further comprises one or more other imaging devices such as a CCD camera and/or fluorescent detector. The droplet detection system further comprises a light source (32) for emitting light onto an illumination zone on the EWOD chip. According to an example embodiment, the light source is configured for illuminating at least part of the EWOD chip top surface. The top surface usually refers to the surface of the EWOD chip receiving the light from the light source. According to an example embodiment, the top surface is the transparent cover. According to another example embodiment, the top surface is the TFT backplane. Because the EWOD chip is substantially transparent to light, a top surface or a bottom surface can be interchangeable in context.

An example EWOD chip as shown in figure lb comprises an EWOD layer (25), a transparent cover (24), and a thin-film transistor (TFT) backplane (26). The EWOD layer (25) and the transparent cover (24) leave a distance therebetween, creating an operation space where the droplets are manipulated and the fluid around droplets is placed. The operation space can be referred as an electrowetting layer. The operation space usually has a height of 10 to 1000 pm to allow droplet manipulation. According to an example embodiment, the transparent cover (24) is a glass cover. According to an example embodiment, the EWOD chip further comprises fluidics such as reservoirs to provide the droplets and the fluids. According to an example embodiment, the reservoirs contain bio-chemical materials such as cell medium, buffers, oils, water, etc. According to an example embodiment, the bio-chemical materials can be supplied by an attached fluidic pump to the reservoirs or can be supplied via pipettes, manually or automatically. Such bio-chemical materials can be contained in tubes, containers, plates, syringes which, for example, are attached to the fluidic pump.

The EWOD layer (25) comprises an electrode array. The electrode array is connected to the TFT backplane The droplets are manipulated by the electrode such that the wetting properties of the droplet are modified and the contact angle of a droplet on the surface of the EWOD chip is changed due to electrostatic effects controlled by the electrode array. The EWOD chip can further comprise carrier(s) (22) for supporting purpose.

The EWOD chip is placed at an intermediate point along a path of the light between the light source and the lens-free imaging device (31). The glass cover (24), the operation space, the EWOD layer (25), and the TFT backplane (26) are substantially transparent such that the light emitted from the light source can be detected by the lens-free imaging device (31). The lens- free imaging device (31) comprises an imager chip and an imager PCB. According to an example embodiment, the time resolution of the lens-free imaging device can be less than or equal to one millisecond.

An example lens-free imaging device (31) has a field-of-view (FOV) of 20 mm². The illumination zone is larger or equal to the FOV of the lens-free imaging device (31). According to an example embodiment, the droplet detection system (103) comprises a plurality of lens-free imaging devices.

The lens-free imaging device (31) captures at least one raw hologram of at least a part of the illumination zone. The hologram image is captured for the entire FOV. It comprises a diffraction pattern over the FOV and the corresponding diffraction pattern to the droplet of interest in the EWOD chip (102). The hologram images can be further reconstructed to 2D and/or 3D images. According to an example embodiment, the droplet detection system comprises a processing unit configured for reconstruction of the hologram image to a 2D and/or 3D image.

And the system or device is configured for providing at least a droplet in the EWOD chip wherein the droplet comprises at least a first and a second object. The system illuminates at least part of the EWOD chip where the droplet is present by the light source (32). At least one first lens-free imaging device obtains at least one image of at least part of the EWOD chip where the droplet is present. The system adapts at least one control signal for manipulating the droplet as a function of the at least one image.

Any of the above-described system (100) or device is from here on referred to as the system or device of the application.

Any of the methods of the first aspect is provided wherein the electrowetting-on-dielectric chip is the system (100) of the second aspect of current application.

A single droplet processor for assisting adoptive cellular therapy

Adoptive cellular therapy or adoptive cell transfer (ACT) is becoming an ever more important treatment paradigm, particularly in the treatment of cancer. ACT refers to the transfer of cells, most typically immune cells, into a patient. These cells may have originated from the patient (autologous therapy) or from another individual (allogeneic therapy). The goal of the therapy is to improve immune functionality and characteristics, and in cancer immunotherapy, to raise an immune response against the cancer. Although T cells from the peripheral blood are most often used for ACT, it is also applied using other immune cell types such as NK cells, tumorinfiltrating lymphocytes or TILs, dendritic cells and myeloid cells.

An example of ACT is chimeric antigen receptor (CAR) T-cell therapy. CAR-T cell therapy has emerged as a novel therapeutic T cell engineering practice, wherein the patient's own immune system is used to fight diseases such as cancer. T cells derived from the patient's blood are genetically engineered in vitro to express artificial receptors that target a specific tumor antigen. In this way, CAR-T cells can directly identify the tumor antigen without the involvement of the major histocompatibility complex (Farhood et al 2019 J Cell Physiol 234, 8509-8521; Ye et al 2017 J Immunol Res 5210459). CAR-T cell therapy has been shown to achieve good treatment outcomes in acute lymphoblastic leukemia (ALL) and diffuse large B cell lymphoma (DLBCL) to make it a promising approach for cancer therapy (Ali et al 2019 Oncologist doi: 10.1634). Hence, it is advantageous to develop means and methods to study the responsiveness of a patient's own immune cells against a tumor of the patient. Moreover, solutions are needed to increase the therapeutic success of CAR-T therapy as its efficiency and effectivity is undermined by the starting T-cell population that is heterogenous and patientspecific and by the heterogenous and poorly defined CAR-T formulations.

Current application addresses these problems by profiling immune cells, more particularly T- cell, effector T-cells and/or CAR-T cells on a single cell level and selecting those cells with a cytotoxic profile on tumor cells in a high-throughput manner. For that purpose, the present application describes an integrated microfluidic droplet-based platform to analyze interactions between cells at the single cell level. The platform is particularly useful for characterization of immune cell-, particularly T-cell-, more particularly CAR-T cell-mediated cytotoxicity against tumor cells, more particularly patient-derived tumor cells.

In another aspect of the application, methods are provided comprising one or more of the following steps:

Encapsulating single immune cell such as CAR-T cells, and tumor cells in microdroplets, more particularly in nanoliter droplets. In a particular embodiment, said encapsulation is performed on a microfluidic chip, more particularly on an EWOD microfluidic chip, even more particularly on the device or system (100) herein disclosed. In another particular embodiment, said encapsulation is performed prior to loading encapsulated cells on an EWOD microfluidic chip, more particularly on the device or system (100) herein disclosed.

Individual handling of the droplets comprising the single cells to move, merge, mix, split individual droplets with cells and assay reagents.

Real-time and label-free monitoring of the viability state of tumor cells before and after contact with a single immune cell such as a CAR-T cell using lens-free imaging, optionally comprising the involvement of artificial intelligence algorithms.

Selection and retrieval of cytotoxic immune cells, for example CAR-T cells.

The technology herein disclosed can be used to evaluate the heterogeneity of immune cell populations with respect to their interactions with other cells, such as to evaluate the potency of an individual patient's immune response against cancer cells of the patient. And importantly, select one, two, three, four, five or more cytotoxic immune cells for generating one, two, three, four, five or more subpopulations that can be used as a treatment for the patient. Non-limiting selection criteria are high cytotoxic potency, production of effective cytokines, level of differentiation, memory-type characteristics, ... As indicated above, CAR-T cell therapy holds great promise in the treatment of cancer. This powerful technique involves genetic engineering of T lymphocytes to enable selective destruction of disease-causing cells. In the current approach, a patient's T cells are genetically engineered to express an antigen-specific antibody fragment fused to activating cytoplasmic T-cell signaling domains. After ex vivo activation and genetic modification of a patient's own T cells, the individually tailored CAR-T cells are then infused into the patient for the selective destruction of cells bearing the targeted antigen.

While TCR-T cells recognize cancer cells by the major histocompatibility complex (MHC) that is sometimes masked in tumors (Fesnak et al 2016 Nat Rev Cancer 16, 566-581), the basic CAR construct consists of an extracellular target cell-specific recognition site associated to an intracellular stimulatory signal for cytotoxic T cell activation to improve targeting efficiency.

Chimeric antigen receptors (CARs) are thus fusion proteins consisting of an antigen-recognition domain and T-cell intracellular signaling domains. Typically, the CAR antigen-recognition domain is an antibody single-chain variable fragment derived from a monoclonal antibody specific for a target antigen (Levine et al., 2017). The CAR intracellular portion contains T-cell signaling domains that activate and potentiate the T-cell response. When the CAR-T cell's antigen-recognition domain interacts with an antigen-bearing cell, the CAR-T cell's internal signaling domains activate CAR-T cells to proliferate, secrete cytokines, and kill the antigenbearing target cell. Accordingly, CAR-T cells can mediate efficient, antigen-specific cell killing without the involvement of the major histocompatibility complex.

CARs are made of three domains: (1) the extracellular portion comprising the antigenrecognition domain, which is typically a single-chain variable fragment antibody fragment, (2) a transmembrane domain that anchors the CAR to the cell membrane, and (3) the intracellular domain that contains a CD3 zeta a signaling domain and costimulatory domains that enhance T-cell proliferation, cytokine release, and killing activity after antigen binding. In first- generation CARs, the intracellular signaling domain consists solely of a CD3z chain, a component of the endogenous T-cell receptor (TCR). These first-generation CARs showed minimal killing and persistence in vivo, likely because of low-level T-cell activation and expansion in response to tumor antigens (Jensen et al., 2010; Till et al., 2008). Subsequent CAR designs have refined the intracellular signaling domain to contain one ore more co-stimulatory domains such as CD28, CD27, 4-1BB (or CD137) and/or OX-40 (or CD134). These second- and third-generation CAR T cells have shown excellent tumor killing and persistence in vivo.

The production of CAR-T cells involves extracting T lymphocytes from the blood of the patient, followed by ex vivo culture to amplify the cells for genetic engineering and cell sorting to isolate the CAR-expressing cells (Levine et al 2017 Mol Ther Clin Dev 4, 92-101). Like all therapeutics, analytic tests are carried out on each CAR-T cell product for quality control purposes. In vitro measurement of CAR expression or cytokine release indirectly reflect CAR-T cytotoxicity, while the xenograft models closely mimic the in vivo scenario, but this method is low throughput and costly.

The microfluidic device or system (100) of the application and the methods of the current application, through their ability to isolate and maintain single cells and pairs of CAR-T and tumor cells make it possible to analyze various cell phenotypes and cell-cell interactions, such as cell viability and/or susceptibility of tumor cells to an antitumor agent.

The system (100) of the application allows the generation of microdroplets comprising one or more objects as described herein. Details of alternative suitable microfluidic devices and methods of using them for co-encapsulation of cells in aqueous droplets in an oil stream can be found in W02017/011819, which is hereby incorporated by reference.

In a third aspect, the methods according to the first aspect are provided wherein the first object in the droplet is an immune cell, more particularly a T-cell, even more particularly a CAR-T cell and the second object in the droplet is a tumor cell. These methods are provided wherein the step of adapting the manipulation of the droplet as a function of the obtained at least one image comprises the steps of classifying the object in the droplet as an immune cell (or a T-cell or a CAR-T cell) or a tumor cell and/or determining the viability status of the tumor cell. Said classification and/or determining steps are performed by a trained machine learning algorithm (for details see below). In one embodiment, the methods of the third aspect are methods for identifying or selecting a cytotoxic immune cell, more particular for identifying or selecting an immune cell that is cytotoxic towards a tumor cell.

In a fourth aspect, a method is provided of analyzing cell-cell interactions comprising the steps of: a. generating a plurality of microdroplets, more particularly a plurality of aqueous microdroplets in oil, on the system (100) of the application, wherein each microdroplet comprising at least one cell of a first type of cells and at least one cell of a second type of cells and wherein the system (100) comprises an electrowetting-on-dielectric chip (102); a droplet detection system (103) comprising a light source (32) for emitting light onto an illumination zone on the EWOD chip and at least one first lens-free imaging device (31) configured for obtaining at least one image comprising one or more droplets in the electrowetting-on-dielectric chip (102); and a control unit (104) configured for receiving the at least one image from the droplet detection system (103) and for sending control signals to the electrowetting-on-dielectric chip (102); or loading a plurality of microdroplets, more particularly a plurality of aqueous microdroplets in oil on said system (100) of the application, wherein each microdroplet comprising at least one cell of a first type of cells and at least one cell of a second type of cells; b. obtaining one or more images of the cells in the microdroplets using the at least one lens-free imaging device (31); c. analyzing the cell-cell interaction using said one or more images.

In one embodiment, the microdroplet comprising at least one cell of a first type of cells and at least one cell of a second type of cells is obtained by preparing a first set of aqueous microdroplets in oil on a microfluidic device, more particularly an EWOD chip, even more particularly the system (100) of the application, each microdroplet comprising at least one cell of a first type of cells and preparing a second set of aqueous microdroplets in oil on a microfluidic device, more particularly an EWOD chip, even more particularly the system (100) of the application each microdroplet comprising at least one cell of a second type of cells and subsequently merging a microdroplet of the first set with a microdroplet of the second set via electrowetting on dielectric (EWOD) configuration for example on the system (100) of the application.

In another embodiment, the microdroplet comprising at least one cell of a first type of cells and at least one cell of a second type of cells is obtained by preparing aqueous microdroplets in oil on a microfluidic device from a cell suspension comprising a plurality of cells of the first type and the second type of cells.

In one particular embodiment, said first type of cells are immune cells, more particularly T-cell, even more particularly CAR-T cells. In another particular embodiment, said second type of cells are tumor cells.

In one embodiment of the methods of the fourth aspect, the system (100) is further specified by any of the embodiments of the system (100) of the second aspect.

In a fifth aspect, a method is provided of identifying or selecting a cytotoxic immune cell or an immune cell that is cytotoxic towards a tumor cell, the method comprising the steps of:

1. providing a system (100) comprising an electrowetting-on-dielectric chip (102); a droplet detection system (103) comprising a light source (32) for emitting light onto an illumination zone on the electrowetting-on-dielectric chip (102) and at least a first lens-free imaging device (31) configured for obtaining at least one image comprising the droplet in the electrowetting-on-dielectric chip (102); and a control unit (104) configured for receiving the at least one image from the droplet detection system (103) and for sending control signals to the electrowetting-on-dielectric chip (102);

2. loading or generating on the system (100) a plurality of microdroplets, more particularly aqueous microdroplets in oil, each microdroplet comprising at least one immune cell and at least one tumor cell; or loading or generating on the system (100) a first set of droplets, more particularly aqueous microdroplets in oil, each microdroplet comprising a single immune cell and loading or generating on the system (100) a second set of droplets, more particularly aqueous microdroplets in oil, each microdroplet comprising a single tumor cell and merging a microdroplet of the first set with a microdroplet of the second set on the electrowetting on dielectric chip (102);

3. obtaining one or more images of the cells in the microdroplet using the first lens-free imaging device (31); 4. determining the viability status of the tumor cell within the microdroplet as a living, dying or dead cell using said one or more images.

In one embodiment, the method comprises a step between step 3 and 4, wherein the step is a step of classifying the cells in the microdroplet as immune cell or tumor cell.

In one embodiment, the method further comprises a step of selecting the microdroplet in which the tumor cell is dying or is dead. In a further embodiment, the method further comprises a step of isolating the immune cell from the microdroplet in which the tumor cell is dying or is dead and/or propagating said immune cell.

In one embodiment, a series of images of the microdroplet or of the cells within the microdroplet are obtained over a period of time using lens-free imaging.

In another embodiment, determining the viability status of the tumor cell as living, dying or dead cell and/or classifying the cells in the microdroplet as immune or tumor cell is performed by a machine learning algorithm. In another embodiment, the analysis of the image data by the machine learning algorithm comprises extracting a feature vector from the image. In yet another embodiment, the image data are normalized prior to feature vector extraction. In a particular embodiment, the image data are image data of a hologram of the cell or cells.

In one embodiment, determining the viability status of the tumor cell as living, dying or dead cell comprises comparing a feature vector extracted from the image data with a predetermined data set characterizing either a living, dying or dead cell.

In one embodiment, classifying the cells in the microdroplet as immune or tumor cell comprises comparing a feature vector extracted from the image data with a pre-determined data set characterizing either an immune cell or tumor cell.

In a particular embodiment, the pre-determined data set is trained using feature vectors extracted from a plurality of living, dying or dead cells. In another particular embodiment, the pre-determined data set is trained using feature vectors extracted from a plurality of immune cells or tumor cells. In a more particular embodiment, said training is supervised. In another more particular embodiment, said training is unsupervised.

In one embodiment of the methods of the fifth aspect, the system (100) is further specified by any of the embodiments of the system (100) of the second aspect. In summary, the methods described herein provide the advantage of identifying immune cells more particularly T-cells or CAR-T cells that can target and/or eliminate the type of tumor that the patient is suffering from. By using a microfluidic device such as the system (100) of the application to identify these immune cells, a plurality of fer example T-cells or CAR-T cells can be tested in parallel in a relatively rapid assay format and specific immune cells of interest can be identified, selected, cultured, and propagated.

Below more detailed information can be found on the different steps of the methods disclosed herein.

Loading tumor cells and immune cells on a microfluidic device

In certain embodiments, the methods herein described comprise the step of loading single cells of two types of cells on or into a microfluidic device. "Loading" as used herein refers to adding or inputting, for example inputting single cells or droplets into a microfluidic device. In one embodiment, one type of cells are tumor cells. In a particular embodiment, said tumor cells are derived from a dissociated cell sample obtained from a tumor sample. In other particular embodiments, said tumor cells are derived from one or more immortalized tumor cell lines.

In another embodiment, the other type of cells are immune cells, more particularly T-cells, even more particularly CAR-T cells. The two types of cells are loaded into the microfluidic device to obtain a set of microdroplets comprising one or more single cells. In one embodiment, said microfluidic device is the system (100) of the application and the droplets are generated in the droplet generation zone as described herein. In another embodiment, said microfluidic device is a separate device compared to the system (100) of the application whereafter the obtained microdroplets can be loaded on the system (100) of the application.

Microfluidic droplets compartmentalize cells and cell-secreted products within e.g. nanoliter volume emulsions, thereby limiting communication between nearest neighbors while allowing interaction between the co-encapsulated cells. These droplets serve as bioreactors for the encapsulated cells and allow appropriate gas exchange necessary to ensure cell viability and functionality. Droplets have been used previously to entrap mammalian and microbial cells and assess responses at the single cell level.

The tumor cells (obtained from tumor cell lines or fractionated from a patient's sample as discussed below) and immune cells that are to be selected can be loaded into the microfluidic device by flowing the cells through an inlet in the device and into the flow region.

As is well known in the art, a population of tumor cells derived from a tumor can be relatively heterogeneous with respect to the morphological and genetic characteristics of individual cells that make up the population. For example, the population may contain tumor stem cells (which may divide slowly) and more differentiated tumor cells (which may divide more rapidly and may contain differing subset of pro-cancer mutations). Marker-based selection and/or cloning of tumor cells can be used to provide more homogeneous populations of cells. Accordingly, in certain embodiments, a plurality of heterogeneous tumor cells may be loaded onto the microfluidic device. Alternatively, individual tumor cells may be selected and cloned before loading onto the microfluidic device. Thus, in other embodiments, a substantially homogeneous population of tumor cells can be loaded into the microfluidic device and used to identify immune cells that bind to and destroy the tumor cells. The substantially homogeneous population can be a tumor cell line derived from the patient providing the at least one tumor or from a different patient.

Microdroplets comprising one or more single cells can be obtained by at least two ways. In one embodiment, immune cells and tumor cells are loaded into the microfluidic device or the system (100) in two separate cell suspensions to obtain a first set of microdroplets comprising one or more single immune cells and a second set of microdroplets comprising one or more single tumor cells. In certain embodiments, the immune cells are loaded into the microfluidic device or the system (100) and moved into a droplet storage zone before the tumor cells are loaded into the microfluidic device or the system (100). In other embodiments, the immune cells are loaded into the microfluidic device or the system (100) and moved into a droplet storage zone after the tumor cells have been loaded into the microfluidic device or system (100) and moved into isolation regions. In still other embodiments, the immune cells and the tumor cells are loaded into the microfluidic device or the system (100) at the same time.

In another embodiment, the immune cells and tumor cells form a single heterogenous cell suspension that is loaded into the microfluidic device or system (100) to obtain a plurality of microdroplets comprising one or more single immune cells, one or more single tumor cells, or the combination thereof.

In certain embodiments, the tumor cells are cultured and/or cloned prior to assaying the immune cells for binding to the tumor cells. Such culturing and/or cloning can be performed within the microfluidic device or prior to loading the tumor cells into the microfluidic device (e.g., using conventional techniques for selecting, culturing, and/ or cloning tumor cells from a tumor sample). Regardless, the tumor cells can be selected, cultured, and/or cloned to a concentration of at least about 1 x 10⁷, 2.5 x 10⁷, 5 x 10⁷, 7.5 x 10⁷, or 1 x 10⁸ cells/ml.

In one embodiment, the droplets are generated prior to loading the droplets on the system (100) of the application. In another embodiment, the droplets are generated on the system (100) of the application.

Movement of single droplets in an EWOD configuration

Once the droplets comprising at least a first type of cells (e.g. immune cells) and/or a second type of cells (e.g. tumor cells) are generated or loaded on the system (100) of the application, the single droplets comprising tumor cells alone, immune cells alone or tumor and immune cells together can be moved into the droplet storage zone of the system. The movement of the cell containing droplets may be accomplished by a variety of means, but for the purpose of this invention the droplets are moved using electrowetting on dielectric (EWOD) configuration. In some embodiments, only one immune cell or one tumor cell is moved on the EWOD device.

Contacting immune cells with tumor cells

In case the microfluidic droplets were obtained using separate cell suspensions of immune cells and tumor cells, both types of droplets are to be brought in close proximity to each other in order to get the droplets fused. "Close proximity" as used herein refers to a "proximal" location that can be within 1 millimeter (mm) (e.g., within 750 pm, within 600 pm, within 500 pm, within 400 pm, within 300 pm, within 200 pm, within 100 pm, or within 50 pm of each other). The merging of the droplets is obtained in the central area of the EWOD chip of the application. Detecting immune cell - tumor cell interaction and determining the viability state of the tumor cells

The herein disclosed methods include the step of detecting whether an immune cell, more particularly a T-cell or CAR-T cell is binding a tumor cell and inducing death or cell lysis of the tumor cell or in other words detecting cytotoxic immune cell-mediated cell lysis.

Binding of immune cells to tumor cells and the subsequent cell death of the tumor cells may be detected in a variety of ways. For the purpose of current application said detection is performed using lens-free imaging.

In the system (100) of the application, the imaging device is an integral part of the system and allows for distinguishing tumor from immune cells and for visualizing the viability state of the encapsulated tumor cells. The image device (e.g. LFI) can periodically image the microfluidic device and detect a change in signal over time. The images can be obtained, for example, every few seconds (e.g., every 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 seconds, or more) or every few minutes (e.g., every 2, 3, 4, 5, 6, 7, 8, 9, 10 minutes, or more). The detection of signal can be manual, such as occurs when a person reviews the images, or automated (e.g., using appropriate image analysis software).

To be compatible with the high-throughput approach of current application a label-free, non- invasive and non-disruptive platform is needed to identify a tumor cell and/or an immune cell in the microdroplet, to monitor the cell-cell interaction between the tumor cell and the immune cell and to classify the viability status of the tumor cell as living, dying or death. Hence, the methods herein disclosed comprise the steps of obtaining LFI data on the encapsulated cells and based thereon classifying the encapsulated cells as a tumor cell or an immune cell; as well as the steps of obtaining LFI data on the encapsulated tumor cell and based on said image data determine the viability status of the tumor cell, more particular determine whether the tumor cell is alive, dying or death.

The analysis of the image data and the decision taking is dealt with by a machine learning algorithm.

Using a machine learning algorithm, which may be a support vector machine (SVM) classifier, decision tree, maximum likelihood classifier, neural networks, or the alike, appropriate decision criteria may be developed with respect to observed features. These features may be embodied in a classification algorithm. The learning process may be either supervised or unsupervised.

In one embodiment, determining the viability or cell type of an encapsulated cell comprises comparing a feature vector extracted from the image data with a pre-determined data or training set characterizing either a living, dying or death cell and/or a first type of cell (e.g. immune cell) or a second type of cell (e.g. tumor cell). Such methods are similar to a supervised classification. In supervised classification, training data containing examples of pre-determined categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the known classes. In one embodiment, the pre-determined data set can be established using a living, a dying or death cell, more particularly a living, a dying or death tumor cell. In another embodiment, the pre-determined data set can be established using different types of cells, for example tumor cells and immune cells, or more particularly tumor cells of specific tumor types and/or specific immune cells. In certain embodiments, the pre-determined data set is trained using feature vectors extracted from a plurality of living, dying or death cells or a plurality of immune cells or tumor cells.

In pattern recognition and machine learning, a "feature vector" is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis.

Methods disclosed herein include analyzing the image data by extracting a feature vector from the image. In one embodiment, extracting a feature vector comprises characterizing the image data using a function having an invariant magnitude in at least one dimension. In one embodiment, the image data is normalized prior to feature vector extraction.

Methods disclosed herein further comprise the step of determining the viability of a cell comprising obtaining image data of a hologram of the cell; analyzing the image data; and determining the viability status of the cell based on said analysis.

The training set can be classified as living, dying or death or as tumor cell or immune cell by using techniques well known in the art. There are many well-known experimental techniques or commercially available kits useful for classifying a cell as viable or non-viable or for classifying cell types. New data may then be applied to the learning mechanism, which then classifies the new data using the learned relationships. Examples of supervised classification processes include linear regression processes (e.g., multiple linear regression (LR), partial least squares (PLS) regression and principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).

In other embodiments, the classification models that are created can be formed using unsupervised learning methods. Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre-classifying the spectra from which the training data set was derived. Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into "clusters" or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other. Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self-Organizing Map algorithm.

The classification models can be formed on and used on any suitable digital computer.

The training data set and the classification models described above can be embodied by computer code that is executed or used by a digital computer.

Finally, droplets in which the immune cells do not interact with the cancer cell or do not destroy the cancer cell may be discarded from the microfluidic device.

In certain embodiments, droplets in which the immune cells bind and/or destroy the cancer cell can be exported from the microfluidic device. For example, in order to isolate the immune cells for either sequencing or prepare a population of cloned immune cells. Examples

Example 1. Co-encapsulation of immune cells and tumor cells

In order to study the interaction between tumor cells and immune cells, more particularly effector T-cells or CAR-T cells, both types of cells are brought into proximity to each other. In a first method, a cell suspension composed of a mixture of effector T cells/chimeric antigen receptor (CAR)-T cells (b) and target tumor cells (a) is encapsulated in picoliter-sized droplets (Figure 7). Although multiple standard practices can be used, in this example droplets were generated on an EWOD chip. After loading the heterogenous cell suspension into a reservoir of the EWOD chip, one or multiple electrodes located adjacent to the reservoir are switched on, whereby the cell suspension fluid is extruded out of the reservoir onto the main chip surface. One or multiple electrodes are then switched off such that the extruded liquid is 'pinched' off and a droplet containing one or multiple CAR-T and one or multiple tumor cells is created. This process is repeated x-fold to generate 50-100K picoliter-sized droplets containing a single or multiple CAR-T cells (b) and a single or multiple tumor cells (a). In a preferred case, the droplets contain a single CAR-T cell (b) together with one or multiple tumor cells (a). The EWOD chip is preferably a transparent imaging chip composed of two or more transparent layers, such that droplets are contained in the open space between the transparent layers. Droplets are arranged in a monolayer for imaging.

Example 2. Generating droplets from homogenous cell suspensions

Alternatively, separate pools of encapsulated cells are merged to each other (Figure 8). In this case, a cell suspension composed of effector T cells/chimeric antigen receptor (CAR)-T cells (b) is introduced into reservoir 1 of an EWOD chip, while a cell suspension composed of tumour cells (a) is introduced into reservoir 2 of the EWOD chip. At each reservoir, one or multiple electrodes located adjacent to the reservoir are switched on, whereby cell suspension fluid is extruded out of the reservoir onto the main chip surface. One or multiple electrodes are switched off such that the extruded liquid is 'pinched' off and a droplet containing one or multiple T-cells (b) and a droplet containing one or multiple tumour cells (a) is created. Subsequently, the picoliter-sized droplets are moved to one (same) location on the chip through repeated actions of switching on and off electrodes (enabling droplet actuation), whereby droplets are merged (Figure 8). This process is repeated x-fold to generate 50-100K picoliter-sized droplets containing preferably a single effector T-cell / CAR-T cell (b) and a single or multiple tumour cells (a).

Example 3. Lens-free imaging of cell-cell interactions (cancer cell cytolysis)

The droplets generated according to the procedure of Examples 1-2 are then actuated across the chip for screening. The screening of the interaction between the immune cells and tumour cells is performed by lens-free imaging (LFI). LFI enables an automatically identification and classification of cells by providing a digital holographic image of the cells. LFI software supplemented with artificial intelligence (Al) driven algorithms can distinguish various types of effector T cells and various types of tumour cells present in the droplet in a label-free manner.

The transparent chip holding the 50-100K picoliter-sized droplets containing a combination of effector T-cell / CAR-T and tumour cells is placed over the sensor of the lens-free imager such that the entire chip can be imaged by the lens-free imager. Dedicated imaging software is used to capture high-resolution holographic images of cells within each droplet. Cancer cells that undergo cytolysis are characterized by membrane blebbing and rupture. The trained LFI software is able to recognize these morphological changes and therefore distinguishes live, dying and dead cells from each other. The immune cells are observed in-real time interacting with tumour cells contained within a single same droplet. The droplets in which the tumour cells are destroyed are selected for subsequent recovery and analysis of the effector T cell or CAR-T cell.

Claims

1. A method for droplet manipulation comprising the steps of: a. providing at least a droplet in an electrowetting-on-dielectric chip wherein the droplet comprises at least a first and a second object; b. illuminating at least part of the electrowetting-on-dielectric chip where the droplet is present; c. obtaining at least one image from at least a first lens-free imaging device of at least part of the electrowetting-on-dielectric chip where the droplet is present; d. adapting a manipulation of the droplet as a function of the at least one image.

2. The method according to claim 1, wherein the step (a) of providing at least a droplet in an electrowetting-on-dielectric chip wherein the droplet comprises at least a first and a second object, comprises: a. 1 merging at least a first droplet comprising the first object and a second droplet comprising the second object.

3. The method according to claim 2, wherein the step (a.l) of merging at least a first droplet comprising the first object and a second droplet comprising the second object is preceded by a step of: z. selecting the first and second droplets from corresponding a first and second groups of droplets wherein the first and second droplets comprise a predetermined amount of objects in each droplet, optionally one object in each droplet.

4. The method according to claim 3, wherein the step (z) of selecting the first and second droplets from corresponding a first and second groups of droplets wherein the first and second droplets comprise only one object in each droplet is preceded by a step of: z.O generating the first and second groups of droplets.

5. The method according to any of the preceding claims, wherein at least one of the first and second objects is a biological cell.

6. The method according to claim 5, wherein the first object is a tumor cell and the second object is an immune cell.

7. The method according to any of the preceding claims, wherein the at least one image is a hologram, reconstructed two-dimensional or reconstructed three- dimensional image.

8. The method according to claim 7, wherein a plurality of images is obtained and wherein the plurality of the images comprises hologram, reconstructed two- dimensional and/or reconstructed three-dimensional image.

9. The method according to claims 3 or 4 further comprising a step of: obtaining at least one image from a second lens-free imaging device of at least part of the electrowetting-on-dielectric chip where the step (z) of selecting the first and second droplets from corresponding a first and second groups of droplets wherein the first and second droplets comprise a predetermined amount of objects in each droplet, optionally one object in each droplet, and/or step (z.O) of generating the first and second groups of droplets is operated, is/are performed; optionally, wherein the second lens-free imaging device provides a lower resolution than the first lens-free imaging device.

10. The method according to any of the preceding claims wherein the step (d) of adapting a manipulation of the droplet as a function of the at least one image, comprises adapting the manipulation of the droplet as a function of at least a variable relating to at least a feature of the first and/or second object; optionally, wherein the at least a feature is selected from morphological characteristic features and behavioral features.

11. The method according to any of the preceding claims wherein the step (d) of adapting a manipulation of the droplet as a function of the at least one image, comprises adapting the manipulation of the droplet as a function of at least a variable relating to a change of at least a feature of the first and/or second object over a period of time; optionally, wherein the at least a feature is selected from morphological characteristic features and behavioral features.

12. The method according to any of the preceding claims, wherein the pixel dimension of the first lens-free imaging device is in the range of 0.2 to 20pm.

13. The method according to claim 9, wherein the pixel dimension of the second lens- free imaging device is in the range of 0.2 to 50pm.

14. A system (100) for droplet manipulation comprising: an electrowetting-on-dielectric chip (102); a droplet detection system (103) comprising a light source (32) and at least a first lensfree imaging device (31) configured for obtaining at least one image comprising the droplet in the electrowetting-on-dielectric chip (102); a control unit (104) configured for receiving the at least one image from the droplet detection system (103) and for sending control signals to the electrowetting- ondielectric chip (102) wherein the system is configured for: a. providing at least a droplet in the electrowetting-on-dielectric chip wherein the droplet comprises at least a first and a second object; b. illuminating at least part of the electrowetting-on-dielectric chip where the droplet is present; c. obtaining at least one image from at least a first lens-free imaging device of at least part of the electrowetting-on-dielectric chip where the droplet is present; adapting at least one control signal for manipulating the droplet as a function of the at least one image.

15. A method of identifying a cytotoxic immune cell comprising the steps of: a. providing a system (100) comprising an electrowetting-on-dielectric chip (102); a droplet detection system (103) comprising a light source (32) for emitting light onto an illumination zone on the electrowetting-on-dielectric chip (102), and at least a first lens-free imaging device (31) configured for obtaining at least one image comprising a droplet in the electrowetting-on- dielectric chip (102); and a control unit (104) configured for receiving the at least one image from the droplet detection system (103) and for sending control signals to the electrowetting-ondielectric chip (102); b. loading or generating on the system (100) a plurality of droplets, each droplet comprising at least one immune cell and at least one biological cell; c. obtaining one or more images of the cells in the droplets using the first lens- free imaging device (31); d. determining the viability status of the tumor cell within the microdroplet as living, dying or dead using said one or more images.

16. The method according to claim 15 wherein the droplet comprising at least one immune cell and at least one tumor cell is obtained by preparing on the electrowetting- ondielectric chip (102) a first set of droplets, each droplet comprising at least one immune cell and preparing a second set of droplets, each droplet comprising at least one tumor cell and subsequently merging on the electrowetting-on-dielectric chip (102) a droplet of the first set with a droplet of the second set.

17. The method according to claim 15, wherein the droplet comprising at least one immune cell and at least one tumor cell is generated on the electrowetting-on- dielectric chip (102) by generating droplets from a cell suspension comprising a plurality of immune cells and a plurality of tumor cells.

18. The method according to any of claims 15-17, wherein said droplets are aqueous droplets in oil.

19. The method according to any of claims 15-18, further comprising a step c' of classifyingthe cells in the droplets as immune cells and/or as tumor cells.

20. The method according to any of claims 15-19, further comprising a step e of identifying the immune cell present in the droplet in which the tumor cell is dying or is dead as a cytotoxic immune cell.

21. The method according to any of claims 15-20, wherein determining the viability status of the tumor cell as living, dying or dead and/or classifying the cells in the microdroplet as immune or tumor cell is performed by a machine learning algorithm.

22. The method according to any of claims 15-21, wherein the immune cells are T-cells.

23. The method according to any of claims 15-21, wherein the immune cells are CAR-T cells.