CN114577972B - Protein marker screening method for body fluid identification - Google Patents
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Abstract
The invention relates to a protein marker screening method for identifying different body fluids. The method comprises the steps of firstly reducing sample complexity by two-dimensional grading, constructing a high-depth body fluid protein library by combining a mass spectrum DDA (Data-dependent acquisition, data dependent acquisition) mode, then carrying out Data acquisition on a large number of body fluid samples by using a DIA/SWATH (Data-independent acquisition, data independent acquisition) mode, screening out candidate biomarkers capable of distinguishing body fluids by combining a tissue specific protein list in an HPA (Human protein atlas, human protein map) item, and further carrying out verification by using an MRM/PRM acquisition mode to obtain protein markers capable of stably realizing body fluid distinguishing. The method can realize the screening of different body fluid markers with high reliability, high sensitivity and high reproducibility, and is widely applied to the identification of body fluids such as circulating blood, menstrual blood, urine, saliva, semen and the like on crime sites.
Description
Technical Field
The invention relates to a protein marker screening method for body fluid identification, which comprises the steps of collecting samples based on DDA and DIA by mass spectrometry, combining HPA project screening and MRM/PRM verification, and can be applied to the identification of body fluids such as circulating blood, menstrual blood, urine, saliva, semen and the like at crime sites.
Background
Forensic serology refers to the differentiation of human body fluids (blood, semen, saliva, vaginal fluid, menstrual blood, etc.) associated with crime scene, and it is important to achieve differentiation of body fluid types rather than identification of the source (Forensic sci.international, 2019,297,350), which is important for the judgment of criminal events. Traditional humoral identification Methods include those based on messenger RNA transcripts (Forensic sci. International, 2015,5, e 441), microRNA expression profiles (Methods mol. Biol.2013,1024, 221), differential epigenetic modifications (Pyrosequencing Methods Protocols 2015,1315,397), raman spectroscopic patterns (biophonics 2014,7,59), and the like, each of which have significant limitations in terms of sensitivity, specificity, and usability.
Proteomics has been widely used for forensic identification because it can identify up to several thousand protein markers in one assay (mol. Systems biol.2011,7,548), increasing the accuracy and specificity required for biomedical analysis. Vansteem constructs a strategy to obtain a protein list of different biological fluids by non-targeted proteomics methods, and screen specific biomarker targeted detection proteins to achieve fluid differentiation (int.j.legal med, 2013,127,287); legg screened 23 biomarkers, and detected markers in circulating blood, vagina/menstrual fluid, semen, urine and saliva in 50 individuals (Electrophoresis 2017,38,833), with specific markers for semen, urine and saliva, but no stable differentiation has been achieved for circulating blood, vagina/menstrual fluid, which is a similar body fluid, mainly because of the sample protein coverage depth and sample-to-sample reproducibility still remain to be improved.
Thus, achieving screening of body fluid markers requires a more specific, high depth, high reproducibility proteomics strategy, which can be based on both theory and practice. The first part screens tissue specific proteins as candidate biomarkers by the human Protein profile project (Protein Sci 2018; 271). HPA maps the localization and expression of proteins in human tissues and cells by immunohistochemistry and immunocytochemistry, and can look at a list of proteins in structures such as organs, organelles, etc., or classify proteins according to the expression level. Thus, tissues associated with the relevant body fluid production or pathways can be queried through the HPA website, and specifically expressed proteins in body fluids can be screened as candidate markers in accordance with annotated biological functions. The second part, the differentially expressed proteins were screened as candidate biomarkers by actual samples. Typical sample collection methods are DDA, or DIA/SWATH, which can obtain complete peptide information by ultra-high speed scanning and secondary fragmentation of all peptide parent ions, enabling a greater dynamic range, higher reproducibility of identification and higher quantitative sensitivity and accuracy than DDA (Nat Commun,2020,02,07,11). Although DIA has more advantages, the data analysis is more difficult due to the complexity of spectrograms, and common methods are to use the analysis of establishing a protein spectrogram base based on DDA and the retrieval based on a sequence database, but the analysis based on the protein base has higher identification and quantitative reproducibility than the retrieval based on the sequence database, the variation coefficient is reduced by more than 30 percent on average, and the deletion value is reduced by more than 35 percent (J protein Res 2020,07,198). Numerous DIA algorithms have been developed, such as MSPLIT-DIA (Nat Methods,2015,12,1212), DIA-NN (Nat Methods,2020,01,171), etc., to achieve better DIA data resolution. Through the two-part combined screening, a body fluid identification marker list with higher reliability and higher specificity can be obtained, and then the marker combination capable of stably distinguishing body fluid is obtained by using the appearance condition of the target monitoring marker in the sample.
Disclosure of Invention
The invention relates to a protein marker screening method for body fluid identification, which comprises the steps of collecting samples based on DDA and DIA/SWATH by mass spectrometry, combining HPA item screening and MRM/PRM verification, and can be applied to the identification of body fluids such as circulating blood, menstrual blood, urine, saliva, semen and the like on crime sites.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
1. adding 100-200mM dithiothreitol to each milligram of protein into the body fluid sample, and reacting for 1.0-2.0h at the temperature of 30-100 ℃ to realize reduction of the protein; adding 100-400mM iodoacetamide into each milligram of protein, and reacting for 30-60min under the dark condition to realize protein alkylation; trypsin is added according to the enzyme/protein ratio of 1:30 to 1:50 (w/w), and enzymolysis is carried out at 37 ℃ overnight to obtain peptide fragments.
2. Mixing all the partial enzymolysis peptide fragments of the body fluid sample, performing first dimension separation by one of reverse phase chromatography, strong cation exchange chromatography, strong anion exchange chromatography, size exclusion chromatography or capillary electrochromatography under alkaline condition, and collecting separated fraction.
3. The collected fractions were analyzed by LC-MS, respectively, and collected using DDA mode: and selecting one of twenty, twenty-one, twenty-two, twenty-three, twenty-four and twenty-five of the abundance ranking according to the parent ions, or performing HCD or CID mode high-energy fragmentation on the parent ions in a three-second one-time cycle scanning mode, respectively detecting the generated child ion information, and searching through a database to obtain a corresponding protein result. All fractions were pooled and pooled using proteomic data retrieval software to obtain a DDA pooled protein list of all fractions.
4. Samples were subjected to DIA/SWATH analysis separately: before DIA/SWATH analysis, carrying out conventional DDA mass spectrum acquisition by using all samples, and respectively optimizing a secondary acquisition window and cycle time of the DIA/SWATH according to peak width and peptide fragment distribution in the result; after setting the acquisition parameters of DIA/SWATH, the samples are respectively subjected to liquid chromatography-mass spectrometry analysis.
5. And comparing the obtained DIA/SWATH data with established body fluid protein libraries by using proteome data retrieval software such as PEAKVIEW, MSPLIT-DIA, DIA-NN, spectronaut and the like capable of performing data independent acquisition and analysis, and comparing corresponding retention time or peaking according to a characteristic spectrogram to obtain a corresponding protein list of each body fluid sample.
6. Screening can be performed in two parts, the first part being obtained by the card DIA/SWATH protein results
A protein with a p-value less than 0.05 and an intensity ratio between samples greater than 2 is used as a candidate biomarker; the second part combined with HPA screening, first enters the The Human Protein Atlas website, selects Tissue maps (Tissue Atlas) associated with the production of the corresponding body fluids, and selects Tissue up-regulating proteins. Proteins common to both parts are screened as candidate biomarkers that can distinguish between different body fluids.
7. The obtained marker list was further validated with MRM/PRM, parent ion or parent-child ion pairs of candidate marker proteins were obtained using Skyline software, and scanning was set up in mass spectrometry to only given target protein signals. And counting the occurrence of candidate markers among all samples, and selecting protein combinations which only occur in one body fluid and have high detection probability to be jointly used as markers for body fluid identification.
The invention has the following advantages:
1. because of the characteristics of DIA/SWATH secondary full scanning, unbiased detection can be carried out on high-low abundance peptide fragments, so that deep coverage can be realized without removing high abundance proteins from a sample, the pretreatment time of the sample is shortened, and the method is more economical and faster.
2. The DDA library construction is used for comparing with database retrieval, so that DIA/SWATH result analysis can be facilitated, the variation coefficient between samples can be further reduced, and the missing value can be reduced.
3. The specificity and the credibility of protein markers obtained by screening are guaranteed from two aspects of theory and practice by combining tissue specific proteins in HPA project and sample result differential expression proteins to screen together.
4. The invention has universality and can realize the identification of body fluids such as circulating blood, menstrual blood, urine, saliva, semen and the like.
Drawings
Figure 1 is a workflow diagram.
Fig. 2 example 1 incorporates the HPA website screening process.
FIG. 3 example 1A DDA acquires a TIC image.
FIG. 4 example 1 sample SWATH acquires a TIC image.
Detailed Description
Example 1
1. Protein denaturation reduction alkylation and enzymolysis
The body fluid samples are circulating blood and menstrual blood, firstly, 8M urea is used for protein denaturation, and 100mM dithiothreitol is added into each milligram of protein in the sample for reaction for 1.0h at 65 ℃ so as to realize reduction of the protein; adding 200mM iodoacetamide into each milligram of protein, and reacting for 20min under the dark condition to realize protein alkylation; trypsin was added at an enzyme/protein ratio of 1:30 (w/w) and the resulting peptide was digested overnight at 37 ℃.
DDA library construction
Mixing the enzymolysis peptide segments, and then carrying out first-dimension chromatographic separation: the mobile phase A is acetonitrile solution with volume concentration of 2% and aqueous solution with volume concentration of 0.1% formic acid, and the mobile phase B is acetonitrile solution with volume concentration of 98% and aqueous solution with volume concentration of 0.1% formic acid. The chromatographic conditions were set at a flow rate of 1. Mu.L/min, a gradient of 0 to 10min using 100% phase A, 10min to 11min100% A to 95% A,11min to 111min linear gradient separation 100min from 95% A to 75% A, 111min to 126min linear gradient separation 15min from 75% A phase to 65% A phase.
LC-MS analysis was performed after completion of chromatographic separation using eksig M5 μl liquid phase coupled AB SCIEX 5600 mass spectrometry. The mass spectrum acquisition uses a DDA data acquisition mode, and peptide ions with the mass range of 300-1800m/z are selected at the first stage; secondary mass spectrometry 40 ions with the strongest primary mass spectrometry signals are selected for secondary fragmentation, and the fragmentation mode is CID (collision induced dissociation). All samples were assayed in 3 replicates.
After the mass spectrum data acquisition is completed, searching a database by using Protein Pilot, and setting parameters: the database was uniprot human library, the protease was trypsin, and the FDR values for protein and peptide fragments were less than 0.1%. And (3) searching all fractions together, wherein 1423 obtained proteins are protein libraries established by DDA.
SWATH analysis
All body fluid mixed samples are firstly subjected to one-needle mass spectrum DDA analysis, and parameters of liquid phase and mass spectrum are the same as parameters used for library construction. According to the DDA primary parent ion mass distribution, SWATH mass acquisition windows are optimized, 80 windows are provided, 3 seconds are circulated once, and each sample is repeatedly measured for 3 times by using the SWATH method.
The resulting SWATH data were processed using Peakview software to extract specific peptide fragments based on retention time to obtain intensity information corresponding to different protein intensity information, resulting in 1325 proteins per sample.
4. Candidate marker screening
Screening is carried out in two parts, wherein the P-value in the result of the first part passing through the SWATH protein is less than 0.05, and the protein with the intensity ratio between samples being more than 2 is used as a candidate biomarker, such as P09466, P54108, Q9BQR3 and the like; the second part is combined with HPA screening, firstly enters The Human Protein Atlas website, selects tissue map of endometrium, and selects tissue up-regulating proteins such as P09466, P54108, etc.; the peripheral blood was not screened for tissue-specific proteins. Proteins common to both fractions, such as P09466, P54108, were screened as candidate biomarkers that can distinguish between menstrual blood and circulating blood.
MRMhr validation of
MRMhr verification is performed by taking body fluid samples respectively, parent ion or parent ion-child ion pairs of candidate marker proteins are obtained by using Skyline software, and only given target protein signals are scanned in a mass spectrometry method. The occurrence of the candidate marker among all samples is counted, the protein combination which only occurs in one body fluid and has high detection probability is selected, for example, the combination of the proteins P09466 and P54108 only occurs in menstrual blood, and the protein combination does not occur in peripheral blood, so that the candidate marker can be used as a marker for distinguishing menstrual blood from circulating blood.
Example 2
1. Protein denaturation reduction alkylation and enzymolysis
The body fluid samples are saliva and semen, firstly, 8M urea is used for carrying out protein denaturation, and 100mM dithiothreitol is added into the sample according to each milligram of protein, and the reaction is carried out for 1.0h at 65 ℃ so as to realize the reduction of the protein; adding 200mM iodoacetamide into each milligram of protein, and reacting for 20min under the dark condition to realize protein alkylation; trypsin was added at an enzyme/protein ratio of 1:30 (w/w) and the resulting peptide was digested overnight at 37 ℃.
DDA library construction
Mixing the enzymolysis peptide segments, and then carrying out first-dimension chromatographic separation: the mobile phase A is acetonitrile solution with volume concentration of 2% and aqueous solution with volume concentration of 0.1% formic acid, and the mobile phase B is acetonitrile solution with volume concentration of 98% and aqueous solution with volume concentration of 0.1% formic acid. The chromatographic conditions were set at a flow rate of 1. Mu.L/min, a gradient of 0 to 10min using 100% phase A, 10min to 11min100% A to 95% A,11min to 111min linear gradient separation 100min from 95% A to 75% A, 111min to 126min linear gradient separation 15min from 75% A phase to 65% A phase.
LC-MS analysis was performed after completion of chromatographic separation using eksig M5 μl liquid phase coupled AB SCIEX 5600 mass spectrometry. The mass spectrum acquisition uses a DDA data acquisition mode, and peptide ions with the mass range of 300-1800m/z are selected at the first stage; secondary mass spectrometry 40 ions with the strongest primary mass spectrometry signals are selected for secondary fragmentation, and the fragmentation mode is CID (collision induced dissociation). All samples were assayed in 3 replicates.
After the mass spectrum data acquisition is completed, searching a database by using Protein Pilot, and setting parameters: the database was uniprot human library, the protease was trypsin, and the FDR values for protein and peptide fragments were less than 0.1%. And (3) searching all fractions together, wherein the obtained result is a protein library established by DDA.
SWATH analysis
All body fluid mixed samples are firstly subjected to one-needle mass spectrum DDA analysis, and parameters of liquid phase and mass spectrum are the same as parameters used for library construction. According to the DDA primary parent ion mass distribution, SWATH mass acquisition windows are optimized, 80 windows are provided, 3 seconds are circulated once, and each sample is repeatedly measured for 3 times by using the SWATH method.
The resulting SWATH data were processed using Peakview software to extract specific peptide fragments based on retention time to obtain intensity information corresponding to different protein intensity information, resulting in 995 proteins per sample being quantified.
4. Candidate marker screening
Screening is carried out in two parts, wherein the first part is combined with HPA screening, firstly, a The Human Protein Atlas website is accessed, tissue Atlas is selected, tissues related to semen generation such as testes are selected, and proteins corresponding to up-regulating genes such as Q9Y5R6 and Q8IV76 are selected; tissues associated with saliva production, such as salivary glands, select for proteins corresponding to up-regulated genes, such as Q96DR8. The second fraction passed through the card DIA/SWATH protein results with a P-value less than 0.05 and a ratio of intensities between samples greater than 2 together as candidate biomarkers, such as upregulation protein Q9Y5R6 in semen, upregulation protein P02814 in saliva. Proteins shared by both parts, such as Q9Y5R6, P02814, were screened as candidate markers that can distinguish semen from saliva.
MRMhr validation of
MRMhr verification is performed by taking body fluid samples respectively, parent ion or parent ion-child ion pairs of candidate marker proteins are obtained by using Skyline software, and only given target protein signals are scanned in a mass spectrometry method. The statistical candidate markers appear in all samples, such as Q9Y5R6 appears only in semen and P02814 appears only in saliva, so the statistical candidate markers can be used as markers for distinguishing semen from saliva.
Example 3
1. Protein denaturation reduction alkylation and enzymolysis
The body fluid samples are circulating blood and menstrual blood, firstly 8M urea is used for carrying out protein denaturation, 100mM dithiothreitol is added into each milligram of protein respectively, and the reaction is carried out at 56 ℃ for 1.0 so as to realize the reduction of the protein; adding 100-400mM iodoacetamide into each milligram of protein, and reacting for 30-60min under the dark condition to realize protein alkylation; trypsin is added according to the enzyme/protein ratio of 1:30 to 1:50 (w/w), and enzymolysis is carried out at 37 ℃ overnight to obtain peptide fragments.
DDA library construction
Mixing the enzymolysis peptide segments, and then carrying out first-dimension chromatographic separation: the mobile phase A is acetonitrile solution with volume concentration of 2% and aqueous solution with volume concentration of 0.1% formic acid, and the mobile phase B is acetonitrile solution with volume concentration of 98% and aqueous solution with volume concentration of 0.1% formic acid. The chromatographic conditions were set at a flow rate of 1. Mu.L/min, a gradient of 0 to 10min using 100% phase A, 10min to 11min100% A to 95% A,11min to 111min linear gradient separation 100min from 95% A to 75% A, 111min to 126min linear gradient separation 15min from 75% A phase to 65% A phase.
After the chromatographic separation is completed, LC-MS analysis is carried out, and the mass spectrum model is easy Nano liquid phase coupled QE plus mass spectrum, and a DDA data acquisition mode is used. The primary mass spectrum selects peptide segment ions with the mass range of 300-1800 m/z; the secondary mass spectrum selects 20 ions with the strongest primary mass spectrum signals for secondary fragmentation, the separation window is 2.0Da, and the fragmentation mode is high-energy particle collision. All samples were assayed in 3 replicates.
After the mass spectrum data acquisition is completed, database searching is performed by using Spectronaut, and parameters are set: the database was uniprot human library, the protease was trypsin, and the FDR values for protein and peptide fragments were less than 0.1%. And (3) searching all fractions together, wherein the obtained result is a protein library established by DDA.
DIA analysis
All body fluid mixed samples are firstly subjected to one-needle mass spectrum DDA analysis, and parameters of liquid phase and mass spectrum are the same as parameters used for library construction. According to the DDA primary parent ion mass distribution, a DIA mass acquisition window is optimized, 80 windows are provided, 3 seconds are circulated once, and each sample is repeatedly measured for 3 times by using a DIA method.
The resulting DIA data were processed using spectrobaut software and using a spectral library-based search format, with results that were able to quantify 685 proteins per sample.
4. Candidate marker screening
Screening is carried out in two parts, wherein the P-value in the result of the first part passing through the card DIA protein is less than 0.05, and the protein with the intensity ratio between samples being more than 2 is used as a candidate biomarker, such as P09466, P54108, Q9BQR3 and the like; the second part is combined with HPA screening, firstly enters The Human Protein Atlas website, selects tissue map of endometrium, and selects tissue up-regulating proteins such as P09466, P54108, etc.; the peripheral blood was not screened for tissue-specific proteins. Proteins shared by the two parts, such as P09466 and P54108, are screened and can be used as candidate markers for distinguishing menstrual blood from circulating blood.
MRM/PRM verification
PRM validation was performed by taking body fluid samples separately, using Skyline software to obtain parent ion or parent-child ion pairs of candidate marker proteins, and scanning only given target protein signals was set up in the mass spectrometry method. The occurrence of statistical candidate markers among all samples, such as P09466 and P54108, only occur in menstrual blood, can be used as markers for distinguishing menstrual blood from circulating blood.
Claims (8)
1. A method for screening protein markers for the identification of different body fluids, characterized in that:
1) Carrying out one-dimensional chromatographic separation on 2 or more different body fluid samples subjected to protein pretreatment, then carrying out liquid chromatography-mass spectrometry analysis, obtaining a mass spectrogram of the body fluid samples by adopting a DDA (Data-dependent acquisition, data dependency acquisition) method, and establishing a body fluid protein library through Data retrieval;
2) Respectively carrying out liquid chromatography-mass spectrometry analysis on each body fluid sample after protein pretreatment, obtaining a mass spectrogram of the body fluid sample by using a DIA/SWATH (Data-independent acquisition, data independent acquisition) mode, and obtaining protein information of each body fluid sample by matching with the body fluid protein library in the step 1), so as to obtain difference protein information among 2 or more body fluid samples;
3) Comparing the difference protein information with tissue specific proteins in HPA (Human protein atlas, human protein map) project, screening out common proteins as candidate biomarkers for distinguishing different body fluids;
4) Carrying out liquid chromatography-mass spectrometry analysis on each body fluid sample subjected to protein pretreatment by using candidate biomarkers, and verifying to obtain proteins existing in different body fluid samples, namely, protein markers capable of stably realizing body fluid differentiation;
the step 3) of screening candidate biomarkers which can distinguish body fluids by combining tissue specific proteins in HPA project comprises the following steps: firstly, entering a The Human Protein Atlas website, selecting a Tissue Atlas (Tissue Atlas), selecting a Tissue proteome related to the generation of corresponding body fluid, selecting genes participating in related biological processes according to protein functions in up-regulated genes, finding out corresponding protein sequence numbers, and screening in a DIA result to serve as candidate biomarkers; meanwhile, protein with a stuck value p-value smaller than 0.05 and an intensity ratio difference larger than 2 in the DIA result is used as a candidate marker for distinguishing body fluid.
2. A method according to claim 1, characterized in that: the protein pretreatment step is that 6-8M urea is used for carrying out protein denaturation extraction on a sample; adding 100-200. 200mM dithiothreitol per milligram of protein, and reacting at 30-100 ℃ for 1.0-2.0h to realize reduction of the protein; adding 100-400-mM iodoacetamide into each milligram of protein, and reacting for 30-60min under the dark condition to realize protein alkylation; trypsin is added according to the mass ratio of enzyme to protein of 1:30 to 1:50, and enzymolysis is carried out at 37 ℃ overnight to obtain peptide fragments.
3. A method according to claim 1, characterized in that: the second dimension of the separation in the step 1) uses reversed phase chromatographic separation under acid condition; the first dimension separation uses one of reverse phase chromatography, strong cation exchange chromatography, strong anion exchange chromatography, size exclusion chromatography, capillary electrophoresis chromatography under alkaline conditions.
4. A method according to claim 1, characterized in that: DDA analysis selects one of twenty to forty of the abundance ranking according to parent ions, or a scanning mode of one-time circulation of 2-5 seconds, carries out HCD or CID mode high-energy fragmentation on the parent ions, respectively detects the generated ion information, and searches through a database to obtain a corresponding protein result, namely a body fluid protein library.
5. A method according to claim 1, characterized in that: the DIA analysis secondary fragmentation is continuously provided with a mass-to-charge ratio window with a certain range along with time, all parent ions passing through the window are fragmented, and signals of fragment ions are detected; and (3) comparing the body fluid protein library established by combining the two-dimensional graded DDA results, and providing corresponding peptide fragments by comparing the retention time to obtain a protein list.
6. A method according to claim 1, characterized in that: the MRM/PRM validation procedure is to obtain parent ion or parent ion-child ion pairs of candidate marker proteins using Skyline software, setting up in the mass spectrometry method to scan only a given target protein signal.
7. A method according to claim 1, characterized in that: and screening one or more proteins only appearing in one body fluid according to the appearance of the proteins in the MRM/PRM verification, and taking the one or more proteins appearing in one body fluid as markers for body fluid identification.
8. A method according to claim 1, characterized in that: the different body fluid samples are two or more than three of circulating blood, menstrual blood, urine, saliva and semen.
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| WO2009149072A2 (en) * | 2008-06-03 | 2009-12-10 | The Research Foundation Of State University Of New York At Albany | Identification of body fluids using raman spectroscopy |
| WO2010131641A1 (en) * | 2009-05-11 | 2010-11-18 | 独立行政法人国立成育医療研究センター | Method for determination of condition of cell |
| WO2016196522A1 (en) * | 2015-05-29 | 2016-12-08 | Cedars-Sinai Medical Center | Correlated peptides for quantitative mass spectrometry |
| EP3528263A1 (en) * | 2018-02-15 | 2019-08-21 | Siemens Healthcare GmbH | Providing a trained virtual tissue atlas and a synthetic image |
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| CN108387666A (en) * | 2018-03-01 | 2018-08-10 | 南方科技大学 | A kind of proteomics Mass Spectrometry detection method for urine specimen |
| CN110554134A (en) * | 2018-05-30 | 2019-12-10 | 中国科学院上海生命科学研究院 | Clinical blood sample analysis method and application |
| CN111370072B (en) * | 2020-03-04 | 2020-11-17 | 西湖大学 | Implementation method of molecular omics data structure based on data independent acquisition mass spectrum |
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| WO2009149072A2 (en) * | 2008-06-03 | 2009-12-10 | The Research Foundation Of State University Of New York At Albany | Identification of body fluids using raman spectroscopy |
| WO2010131641A1 (en) * | 2009-05-11 | 2010-11-18 | 独立行政法人国立成育医療研究センター | Method for determination of condition of cell |
| WO2016196522A1 (en) * | 2015-05-29 | 2016-12-08 | Cedars-Sinai Medical Center | Correlated peptides for quantitative mass spectrometry |
| EP3528263A1 (en) * | 2018-02-15 | 2019-08-21 | Siemens Healthcare GmbH | Providing a trained virtual tissue atlas and a synthetic image |
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