CN114034625A - Method and device for accurately counting and classifying plankton based on fine classification - Google Patents
Method and device for accurately counting and classifying plankton based on fine classification Download PDFInfo
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Abstract
The invention provides a method and a device for counting and classifying plankton based on fine classification, wherein the method comprises the following steps: finely grading the plankton counting interval based on the particle size of the plankton cells, and detecting the abundance of the plankton cells in different particle size intervals by using a detection device; meanwhile, the molecular biology analysis is carried out on the collected cells of the plankton in different particle size intervals, and the structural composition, relative abundance and diversity parameters of the plankton in each interval are obtained. The method disclosed by the invention is simple to operate, the sample demand is small, the classification efficiency is high, and the abundance and classification composition information of planktons in different particle size ranges in statistical significance can be obtained, so that the relevant requirements of scientific and efficient development of experimental researches on the particle size compositions, ecological structures and the like of planktons in different particle size ranges are met.
Description
Technical Field
The invention relates to the field of ecological environment research, in particular to a method and a device for accurately counting and classifying plankton based on fine classification.
Background
Plankton is a tiny organism suspended in water and lacking in effective movement capacity, is widely distributed in the global ocean and fresh water bodies, and plays an important role in material circulation and energy flow of aquatic ecosystems. The intensive understanding and research of plankton community structural features, interaction relationships and microbial nutrition structures are important foundations for understanding global water ecosystem changes.
Many of the physiological and ecological functions of plankton are determined by the size of the cell size, which affects growth, metabolism, light energy capture, nutrient uptake, cell sedimentation, and the material circulation and energy flow of plankton in the food network. Therefore, the research on the types of plankton under different particle size scales has very important significance.
However, in the prior art, although observation of the morphological structure of plankton and identification of the counting type can be completed by a microscope, the counting method is to complete identification of plankton type based on morphological structure observation, certain subjective interference factors exist, and the result of type identification has large personal error. In addition, for plankton of low particle size, the microscopic observations gave rise to a severe underestimation of plankton abundance and biomass. Therefore, the abundance of plankton in different particle size ranges and the composition information of plankton communities in the corresponding particle size ranges cannot be accurately obtained.
Therefore, it is necessary to provide a method for accurately counting and classifying plankton so as to solve the current difficulties in accurately counting and classifying plankton.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides a method and a device for accurately counting and classifying plankton based on fine classification. The method comprises the following specific steps:
in a first aspect, the present invention provides a method for counting and classifying plankton based on fine classification, the method comprising the steps of:
step 1, respectively preprocessing two same samples to be detected to obtain a first sample to be detected and a second sample to be detected;
step 2, according to the particle size interval characteristics of plankton, carrying out two-section type interval counting on the first sample to be detected to obtain the abundance of plankton cells in each interval;
step 3, performing classified filtration on the second sample to be tested based on the subareas to obtain filtrate after classified filtration and plankton cells in each interval;
step 4, carrying out small-particle-size inter-cell sorting collection and counting on plankton in the filtrate after the classified filtration to obtain plankton cells and abundance in small-particle-size inter-cells; wherein the small particle size refers to that the particle size of plankton cells is less than 20 μm, and the small interval refers to an interval with an interval difference value of less than or equal to 10 μm;
and 5, extracting the DNA of the plankton cells obtained by the step filtration and the sorting collection, and performing molecular biology analysis to obtain the structural composition, the relative abundance and the diversity parameters of the plankton in each interval.
Preferably, the pre-treatment comprises a first pre-treatment and a second pre-treatment;
the first pre-processing comprises: pre-filtering one part of the sample to be detected by using a stainless steel sieve with the aperture of 200 mu m, and adding a Rogowski reagent into the pre-filtered filtrate to obtain a first sample to be detected;
the second pre-processing comprises: and pre-filtering the other sample to be detected by using a stainless steel sieve with the aperture of 200 mu m to obtain the second sample to be detected.
Preferably, in the step 2, the two-stage interval counting for the first sample to be tested includes:
standing, precipitating and concentrating the first sample to be detected;
and carrying out two-section interval counting on the first sample to be detected obtained after the standing, precipitating and concentrating treatment.
Preferably, the standing precipitation concentration treatment comprises: and shaking the first sample to be detected uniformly, precipitating, taking the lower-layer precipitate into a volumetric flask, adding 2-4% of formaldehyde solution, and fixing the volume to a preset concentration by using distilled water.
Preferably, in the step 2, the dividing means that the counting interval of 20 μm to 200 μm is divided into two consecutive counting intervals.
Preferably, in the step 3, the step of performing the fractional filtration comprises: and based on the partition between the partitions, sequentially selecting corresponding filter membranes to filter the second sample to be detected, and collecting on the filter membranes to obtain plankton samples in each partition range between the partitions. The filter membrane is preserved for later use.
Preferably, in the step 4, before the step of performing small-particle size intercellular sorting, collection and counting on the plankton in the filtrate after the classified filtration, the method further comprises the step of adding paraformaldehyde with a final concentration of 1% into the filtrate after the classified filtration for fixation.
Preferably, in the step 5, the method for molecular biological analysis is high-throughput sequencing or metagenomic analysis.
In a second aspect, the present invention provides a device for counting and classifying plankton based on fine classification, the device comprising:
the inverted fluorescence microscope is used for counting the first sample to be detected in two-stage type and interval mode according to the characteristic of the particle size interval of plankton to obtain the abundance of the plankton cells in each interval;
the flow cytometry sorter is used for carrying out small-particle-size inter-cell sorting collection and counting on plankton in the filtrate after the classification filtration to obtain plankton cells and abundance in small-particle-size inter-cells;
and the molecular biology analysis instrument is used for carrying out molecular biology technical analysis on the plankton cells obtained by the grading filtration and the sorting collection to obtain the structural composition, the relative abundance and the diversity parameters of the plankton in each interval.
Preferably, the excitation wavelengths for sorting collection and counting by the flow cytometric sorter include 405nm and 488 nm.
Compared with the prior art, the method and the device for counting and classifying plankton based on fine classification at least have the following advantages:
(1) in the method for counting and classifying plankton based on fine classification, provided by the invention, the plankton counting interval is finely classified based on the particle size of plankton cells, so that accurate counting of plankton in different particle size subdivision intervals in a water body is realized, and species identification of plankton in different particle size subdivision intervals is completed at the same time, and classification information parameters such as cell abundance, community structure composition, diversity and the like of plankton in corresponding particle size ranges are obtained.
(2) The method for counting and classifying plankton based on fine classification provided by the invention is simple to operate, has less sample demand and high classification efficiency, and obtains the plankton abundance and classification composition information in different particle size ranges in statistical significance, thereby being capable of meeting the relevant requirements of scientific and efficient development of experimental researches on plankton population particle size compositions, ecological structures and the like in different particle size ranges.
In addition, the method for counting and classifying plankton based on fine classification provided by the invention can be used for distinguishing plankton from other impurities such as silt and the like, so that the interference of impurity particles on the experimental result is avoided.
Drawings
FIG. 1 is a schematic diagram illustrating a method for counting and classifying plankton based on fine classification provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating an overall method of a method for counting and classifying plankton based on fine classification according to an embodiment of the present invention;
FIG. 3 shows a distribution diagram of the number of mixed size standard microspheres in SSC (PB450) according to an embodiment of the present invention;
FIG. 4 shows a particle size distribution diagram of FSC and SSC of mixed particle size standard microspheres provided by an embodiment of the invention;
FIG. 5 shows a quantitative distribution plot of SYBE Green I stained planktonic particles at 488Green (FITC) channel parameters provided by an embodiment of the present invention;
FIG. 6 shows a graph of mean genus composition of eukaryotic plankton within various size fractions provided by an embodiment of the present invention;
FIG. 7 shows eukaryotic plankton abundances for various cross-sections within different size fraction ranges provided by embodiments of the present invention;
FIG. 8 shows the Shannon diversity index of eukaryotic plankton of various cross-sections within the range of different size fractions provided by the examples of the present invention.
Detailed Description
The following examples are provided to further understand the present invention, not to limit the scope of the present invention, but to provide the best mode, not to limit the content and the protection scope of the present invention, and any product similar or similar to the present invention, which is obtained by combining the present invention with other prior art features, falls within the protection scope of the present invention.
The specific experimental procedures or conditions are not indicated in the examples and can be performed according to the procedures or conditions of the conventional experimental procedures described in the prior art in this field. The reagents and other instruments used are not indicated by manufacturers, and are all conventional reagent products which can be obtained commercially.
The inventor researches and discovers that the counting and species identification results of plankton with tiny particle size obtained by microscope observation are not very accurate. In the plankton assay results obtained by microscopic examination, the abundance and biomass of plankton having a cell size of 20 μm or less were severely underestimated.
In order to solve the problems of researching the counting and the type identification of plankton under different particle size intervals, the technical concept provided by the invention is as follows: finely grading the plankton counting interval based on the particle size of the plankton cells, and detecting the abundance of the plankton cells in different particle size intervals by using a detection device; meanwhile, based on the molecular biology analysis of the collected cells of the plankton in different particle size intervals, the structural composition, relative abundance and diversity parameters of the plankton in each interval of fine classification are obtained. Based on the technical conception, the invention provides a method and a device for counting and classifying plankton based on fine classification. The specific implementation content is as follows:
in a first aspect, embodiments of the present invention provide a method for counting and classifying plankton based on fine classification. Fig. 1 is a flow chart illustrating the steps of a method for counting and classifying plankton based on fine classification according to an embodiment of the present invention. Referring to fig. 1, the method includes:
step 1(S1), respectively preprocessing two same samples to be detected to obtain a first sample to be detected and a second sample to be detected;
step 2(S2), according to the particle size interval characteristics of plankton, carrying out two-section type interval counting on the first sample to be detected to obtain the abundance of the plankton cells in each interval;
step 3(S3), performing classified filtration on the second sample to be tested based on the intervals to obtain filtrate after the classified filtration and plankton cells in each interval;
step 4(S4), carrying out small-particle-size inter-cell sorting collection and counting on plankton in the filtrate after the fractional filtration to obtain plankton cells and abundance in small-particle-size inter-cells; wherein the small particle size refers to that the particle size of plankton cells is less than 20 μm, and the small interval refers to an interval with an interval difference value of less than or equal to 10 μm;
and 5(S5), extracting, classifying, filtering, sorting and collecting the DNA of the obtained plankton cells, and carrying out molecular biology analysis to obtain the structural composition, relative abundance and diversity parameters of the plankton in each interval.
When the embodiment of the present invention is implemented, preferably, the pretreatment process in step 1 specifically includes a first pretreatment and a second pretreatment;
wherein the first pre-processing comprises: pre-filtering one sample to be detected by using a stainless steel sieve with the aperture of 200 mu m, and adding a Rogowski reagent into the pre-filtered filtrate to obtain a first sample to be detected;
the second pretreatment comprises: and (4) pre-filtering the other sample to be detected by using a stainless steel sieve with the aperture of 200 mu m to obtain a second sample to be detected.
When the embodiment of the invention is implemented specifically, a stainless steel sieve with the aperture of 200 mu m is used for pre-filtering a water sample to remove impurity particles, zooplankton, debris, silt and the like with the aperture of more than 200 mu m. If plankton growing in colony exists in the sample, such as microcystis, anabaena, synnema and the like, ultrasonic dispersion can be carried out on plankton colony by using an ultrasonic cell crusher and selecting specific power, time and ultrasonic frequency, so that the plankton colony becomes a single-cell dispersion state for subsequent analysis.
In the embodiment of the present invention, preferably, in step 2, the two-stage interval counting of the first sample to be measured includes:
standing, precipitating and concentrating a first sample to be detected;
and carrying out two-section interval counting on a first sample to be detected obtained after standing, precipitating and concentrating treatment.
When the embodiment of the present invention is implemented, preferably, the standing, precipitating and concentrating process includes: shaking the first sample to be detected evenly and then precipitating, taking the lower layer precipitate into a volumetric flask, adding 2-4% formaldehyde solution, and fixing the volume to the preset concentration by using distilled water.
When the embodiment of the invention is implemented specifically, the first sample to be detected is subjected to standing precipitation for 24-48 h, then the lower-layer precipitate is taken out and put into a volumetric flask, and 2-4% formaldehyde solution is added for fixation, so that the sample can be preserved for a long time, and meanwhile, distilled water is used for fixing the volume to the preset concentration.
In this embodiment, the specific value of the preset concentration may be set according to a requirement (mainly, the requirement refers to an optimal concentration for microscopic observation and counting) of an operator when performing microscopic observation and counting on the plankton cells in the first sample to be tested, and is not specifically limited in this embodiment.
When the embodiment of the present invention is implemented, preferably, in step 2, the dividing means dividing the 20 μm-200 μm counting interval into two consecutive counting intervals.
In the specific implementation of the embodiment of the invention, different detection instruments are considered to have different detection accuracies for plankton cells with different particle sizes. Meanwhile, in order to collect enough samples in different intervals for subsequent DNA extraction, the set intervals are not too many. Therefore, the invention selects a microscope to count the plankton with the cell size of 20-200 μm in two-stage interval. For example, two counting intervals that are continuous between the partitions are divided into 20-100 μm, 100-200 μm, or 20-65 μm, 65-200 μm, which is not limited herein.
In the embodiment of the present invention, preferably, in step 3, the step of performing graded filtering includes: and based on the partition between the partitions, filtering the second sample to be detected by sequentially selecting corresponding filter membranes, and collecting on the filter membranes to obtain plankton samples in each partition range between the partitions. The filter membrane is preserved for later use.
In the implementation step, in order to unify two continuous counting intervals partitioned by sub-areas and the particle size interval of the plankton sample obtained by classified filtration and collection so as to obtain the precise structural composition, relative abundance and diversity parameters of the plankton in each interval subsequently, the inventor divides 20-200 μm into two continuous counting intervals, and during classified filtration, the filter membrane selection is carried out by partitioning the interval into references, and the plankton sample in each partition range between the sub-areas is collected. For example, when two counting intervals continuous between the sub-regions are divided into 20-100 μm and 100-200 μm, the second sample to be tested is vacuum filtered by using filter membranes with the pore diameters of 100 μm and 20 μm respectively through classified filtration, and plankton in the particle size range (100-200 μm and 20-100 μm) consistent with the two counting intervals continuous between the sub-regions and the filtrate filtered by the 20 μm filter membrane are collected in sequence.
After the implementation step is completed, the method further comprises: paraformaldehyde with a final concentration of 1% was added to the filtrate after the fractional filtration. In specific implementation, the filtrate filtered by the 20-micron filter membrane is used for detecting the abundance and the particle size distribution of the floating biological cells with the cell particle size smaller than 20 microns, and paraformaldehyde with the final concentration of 1% is added into the filtrate for fixation before detection.
When the embodiment of the present invention is implemented, preferably, in step 4, the plankton in the filtrate after the step filtration is sorted, collected and counted in a small particle size cell-to-cell manner, so as to obtain plankton cells and abundance in the small particle size cell-to-cell manner.
When the embodiment of the invention is implemented, the filtrate filtered by the 20 μm filter membrane can be selectively diluted or concentrated according to the concentration of plankton cells in the filtrate to achieve the ideal cell concentration (about 10)9cell/L) to facilitate accurate detection and counting.
When the embodiment of the invention is implemented specifically, the flow cytometer is selected to sort and count the plankton cells with the cell particle size smaller than 20 microns, so that in order to improve the sorting and counting accuracy and collect enough samples in different intervals for subsequent DNA extraction, the set intervals are not too many. Therefore, the embodiment of the invention divides the plankton counting interval smaller than 20 μm into a plurality of smaller intervals by using the standard microspheres for sorting and counting so as to obtain the abundance of the plankton cells in a plurality of small intervals (interval difference is less than or equal to 10 μm) within the range of small particle size (particle size is less than 20 μm). When the embodiment of the present invention is implemented, preferably, in step 5, the method for molecular biology analysis is high-throughput sequencing or metagenomic analysis.
In the specific implementation of the embodiment of the invention, after the filtration by stages, the plankton cells collected on the filter membrane and the plankton cells in a plurality of small intervals (interval difference is less than or equal to 10 microns) within a small particle size (particle size is less than 20 microns) range selected by a flow cytometer are respectively subjected to DNA extraction, and the extracted DNA is subjected to high-throughput sequencing or metagenome analysis method and the like to obtain parameters such as plankton community structure composition, relative abundance, diversity, functionality and the like within different particle size ranges.
In a second aspect, the present invention provides a device for counting and classifying plankton based on fine classification, the device comprising:
the inverted fluorescence microscope is used for counting the first sample to be detected in two-section type and interval mode according to the characteristic of the particle size interval of plankton to obtain the abundance of the plankton cells in each interval;
the flow cytometry sorter is used for carrying out small-particle-size inter-cell sorting collection and counting on plankton in the filtrate after the step filtration to obtain plankton cells and abundance in small-particle-size inter-cells;
and the molecular biology analysis instrument is used for carrying out molecular biology technical analysis on the plankton cells obtained by grading filtration and sorting collection to obtain the structural composition, relative abundance and diversity parameters of the plankton in each interval.
In the embodiment of the present invention, preferably, the excitation wavelengths for sorting, collecting and counting by the flow cytometer include 405nm and 488 nm;
adjusting the power of dual-wavelength excitation and analyzing the voltage of a detection channel to generate a forward angle scattering light parameter FSC (forward Scatter) and a side angle scattering light parameter SSC (side Scatter) for representing micron-sized plankton cells; the parameter MALS of scattering light in the middle angle is additionally generated and used for characterizing the nano-scale plankton cells.
Wherein, before the plankton is sorted, collected and counted by a flow cytometry sorter, the plankton needs to be dyed by using nucleic acid dye excited by 488-wavelength laser such as SYBE Green I, and the plankton and inorganic particles are distinguished by generating a Green fluorescence intensity 488Green parameter.
The implementation of the present invention is illustrated below by specific examples, fig. 2 shows a schematic diagram of the overall method of the method for counting and classifying plankton based on fine classification provided by the embodiment of the present invention, and specific example 1 is described below with reference to fig. 2.
Example 1:
1) sample collection and pretreatment
Setting 8 sampling sections as TC, SHN, SHB, CHQ, CHH, ZHB, DAS and XHS in the alignment line trunk canal, pre-filtering 2 water samples with a stainless steel sieve with the aperture of 200 mu m, adding 10mL of Luge reagent into one 1.0L of water sample for fixation, placing in an incubator for low-temperature dark preservation, and performing microscopic examination and quantitative analysis on phytoplankton; vacuum filtering another 5.0L water sample with filter membrane with pore diameter of 100 μm and 20 μm, collecting plankton with particle diameter of 100-; filtering with 20 μm filter membrane, adding 1% paraformaldehyde into the filtrate, fixing, and storing in incubator at low temperature in dark place for flow cytometry separation and quantitative analysis of phytoplankton.
2) Microscopic examination
After shaking the fixed water sample of the Rogo reagent, 1.0L of the sample was poured into a 1.0L graduated cylinder and allowed to settle for 24 hours. The supernatant phytoplankton-free supernatant was carefully siphoned off using a siphon, leaving 30-50mL of sediment to be transferred to a 50mL volumetric flask. The cylinder was rinsed with a small amount of the above clear solution and the rinse was also transferred to a 50mL volumetric flask and repeated three times. Adding 2% -4% formaldehyde solution into the volumetric flask for fixation so as to be beneficial to long-term storage of the sample, and finally fixing the volume to 50mL by using clear liquid. After the concentrated and precipitated sample was sufficiently shaken up, 0.1mL of the sample was aspirated by a micropipette, and the aspirated sample was injected into a 0.1mL plankton counting frame (surface area 20X 20mm)2Divided into 100 cells). Care was taken to cover the coverslip (2 × 20 × 20mm) with no air bubbles inside the count box and no sample overflow the count box. The phytoplankton in the plankton count box was observed under an inverted fluorescence microscope.
3) Flow cytometry analysis and sorting
The samples pretreated with 20 μm filters and fixed with paraformaldehyde were analyzed by flow cytometric sorting system (Asia SORP, BD, USA) to adjust the sample concentration to about 109cell/L, detecting the abundance and the particle size distribution of phytoplankton less than 20 microns in the sample. Adjusting the power of dual-wavelength excitation and analyzing the voltage of a detection channel to generate a forward angle scattering light parameter FSC and a lateral angle scattering light parameter SSC which are used for representing micron-sized plankton cells; additionally generating a medium angle scattering light parameter MALS for characterizing the nanoscale plankton cells; the analysis requires the use of SYBE Green I et al 48The nucleic acid dye excited by 8-wavelength laser dyes plankton, and distinguishes plankton from inorganic particles by generating Green fluorescence intensity 488Green parameter. Data obtained by flow cytometry sorting System, use
The software performs the analysis.
The mixed particle size standard microspheres comprise nano-scale and micron-scale highly uniform polystyrene standard microspheres. Nanoscale standard microspheres may have optional sizes including 20nm, 100nm, 200nm and 500nm (F13839, Molecular Probes, USA). The micron-sized microspheres may have optional dimensions including: 1 μm, 2 μm, 4 μm, 6 μm, 10 μm and 15 μm. And selecting cells with Green fluorescence 488Green channel parameters in a particle size interval divided by standard microspheres for plankton analysis counting, and selecting cells with Red fluorescence 488Red channel parameters for plankton analysis counting.
In addition, 4 1.5ml centrifugation collection tubes are used at the sorting end for cell collection, the concentration of the collected cells is 50 ten thousand cells per milliliter, 100 mu l of PBS buffer solution is added into the collection tubes in advance, and when the number of the collected cells in the particle size interval with the minimum cell content reaches a certain number to support high-throughput sequencing, the sorting is stopped.
FIG. 3 shows a distribution diagram of the number of mixed size standard microspheres in SSC (PB450) according to an embodiment of the present invention; the micro plankton can be divided into 2 particle size ranges of 200-500 nm and 500-1000 nm according to the nano standard microsphere.
FIG. 4 shows a particle size distribution diagram of FSC and SSC of mixed particle size standard microspheres provided by an embodiment of the invention; as shown in figure 4, the plankton is analyzed and sorted by selecting standard microspheres with different particle sizes, the particle size is selected to be 1 μm, 4 μm, 10 μm and 15 μm, and the micro plankton is divided into 4 particle size intervals according to the mixed standard microspheres, wherein the particle size intervals are respectively 1-4 μm, 4-10 μm, 10-15 μm and 15-20 μm.
FIG. 5 shows a quantitative distribution plot of SYBE Green I stained planktonic particles at 488Green (FITC) channel parameters provided by an embodiment of the present invention. As shown in FIG. 5, the selection has 488Greenn channel intensity (> 10)3) Sorting plankton cells in the size range of 488 channels with strength less than 103As undyed inorganic particles.
4) High throughput sequencing
According to the specification of a DNA extraction Kit (FastDNASpin Kit for Soil), carrying out DNA extraction on plankton cells with different particle size ranges which are selected and collected by a filter membrane with 100 micrometers (or other pore sizes larger than 20 micrometers) and 20 micrometers graded filtration and a flow cytometer (the specific extraction step is carried out according to the specification of the Kit), selecting a design primer (TAReuk 454F: CCAGCASCYGCGGTAATTCC, TAReukR: ACTTTCGTTCTTGATYRA) of an 18S rDNA gene V4 region for PCR amplification, and carrying out sequencing on a Illumina Miseq high-throughput sequencing platform after the PCR amplification product is purified to obtain double-end original sequence data.
The method comprises the steps of performing quality control on original data by using Fastp software, and splicing paired reads into a sequence by using Flash software according to an overlap relation between PE reads (paired-end reads). The sequence alignment tool Ucluster is called by using Qiame (quantitative instruments into microbial technology) software, and the obtained sequences are merged and OTU clustered according to 97% of sequence similarity.
Comparing the OTU representative sequences with template sequences of a Silva database (http:// www.arb-Silva.de), and performing taxonomic annotation on each OTU representative sequence at the levels of kingdom, phylum, class, order, family, genus and species by using RDP classifier Bayesian algorithm (classification confidence coefficient of 0.7).
FIG. 6 shows a graph of mean genus composition of eukaryotic plankton within various size fractions provided by an embodiment of the present invention. As can be seen from FIG. 6, the composition of plankton communities in different particle size ranges is significantly different, the percentage of the green algae norak _ Chlorophyceae is more than 50% in the ranges of 1-2 μm, 2-6 μm, 10-20 μm and 100-200 μm, which is the first dominant species, and the percentage of the green algae is reduced in the micron-sized particle size ranges (200-500 nm and 500-1000 nm). Therefore, the composition of the plankton is analyzed according to the particle size, so that the dominant species in different particle size ranges can be obtained in a refined mode, the key species can be identified, and the method has important significance for monitoring the ecological environment.
FIG. 7 shows the abundance of eukaryotic plankton at various cross-sections within the range of different size fractions provided by the examples of the present invention. As can be seen from FIG. 7, the abundance of plankton in the small particle size interval (less than 20 μm) is significantly higher than that in the large particle size interval (20 μm-200 μm), and the spatial difference in particle size composition of plankton with different sampling cross sections less than 20 μm is significant, which indicates that the abundance of plankton in different interval ranges can be counted through fine classification.
FIG. 8 shows the Shannon diversity index of eukaryotic plankton of various cross-sections within the range of different size fractions provided by the examples of the present invention. As can be seen from FIG. 8, the Shannon diversity index of plankton of 15-100 μm is the highest, and the Shannon diversity index of plankton of 100-200 μm is the lowest, but the Shannon diversity index of plankton of each particle size does not show significant difference.
The method and the device for counting and classifying plankton based on fine classification provided by the invention are described in detail, the principle and the implementation mode of the invention are explained by applying specific examples, and the description of the examples is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for counting and classifying plankton based on fine classification, which is characterized by comprising the following steps:
step 1, respectively preprocessing two same samples to be detected to obtain a first sample to be detected and a second sample to be detected;
step 2, according to the particle size interval characteristics of plankton, carrying out two-section type interval counting on the first sample to be detected to obtain the abundance of plankton cells in each interval;
step 3, performing classified filtration on the second sample to be tested based on the subareas to obtain filtrate after classified filtration and plankton cells in each interval;
step 4, carrying out small-particle-size inter-cell sorting collection and counting on plankton in the filtrate after the classified filtration to obtain plankton cells and abundance in small-particle-size inter-cells; wherein the small particle size refers to that the particle size of plankton cells is less than 20 μm, and the small interval refers to an interval with an interval difference value of less than or equal to 10 μm;
and 5, extracting the DNA of the plankton cells obtained by the step filtration and the sorting collection, and performing molecular biology analysis to obtain the structural composition, the relative abundance and the diversity parameters of the plankton in each interval.
2. The method according to claim 1, wherein in the step 1, the pretreatment includes a first pretreatment and a second pretreatment;
the first pre-processing comprises: pre-filtering one part of the sample to be detected by using a stainless steel sieve with the aperture of 200 mu m, and adding a Rogowski reagent into the pre-filtered filtrate to obtain a first sample to be detected;
the second pre-processing comprises: and pre-filtering the other sample to be detected by using a stainless steel sieve with the aperture of 200 mu m to obtain the second sample to be detected.
3. The method according to claim 1, wherein in step 2, said two-stage interval counting of the first sample to be tested comprises:
standing, precipitating and concentrating the first sample to be detected;
and carrying out two-section interval counting on the first sample to be detected obtained after the standing, precipitating and concentrating treatment.
4. The method according to claim 3, wherein the still standing precipitation concentration treatment comprises: and shaking the first sample to be detected uniformly, precipitating, taking the lower-layer precipitate into a volumetric flask, adding 2-4% of formaldehyde solution, and fixing the volume to a preset concentration by using distilled water.
5. The method according to claim 1 or 3, wherein in the step 2, the partition refers to dividing the 20 μm-200 μm counting interval into two consecutive counting intervals.
6. The method of claim 1, wherein in step 3, the step of fractionally filtering comprises: and based on the partition between the partitions, sequentially selecting corresponding filter membranes to filter the second sample to be detected, and collecting on the filter membranes to obtain plankton samples in each partition range between the partitions. The filter membrane is preserved for later use.
7. The method according to claim 1, wherein in step 4, before the step of performing small-particle size inter-cell sorting collection and counting on the plankton in the filtrate after the classified filtration, the method further comprises the step of adding paraformaldehyde with a final concentration of 1% into the filtrate after the classified filtration for fixation.
8. The method of claim 1, wherein in the step 5, the molecular biology analysis method is high-throughput sequencing or metagenomic analysis.
9. An apparatus for counting and classifying plankton based on fine classification, the apparatus comprising:
the inverted fluorescence microscope is used for counting the first sample to be detected in two-stage type and interval mode according to the characteristic of the particle size interval of plankton to obtain the abundance of the plankton cells in each interval;
the flow cytometry sorter is used for carrying out small-particle-size inter-cell sorting collection and counting on plankton in the filtrate after the classification filtration to obtain plankton cells and abundance in small-particle-size inter-cells;
and the molecular biology analysis instrument is used for carrying out molecular biology technical analysis on the plankton cells obtained by the grading filtration and the sorting collection to obtain the structural composition, the relative abundance and the diversity parameters of the plankton in each interval.
10. The device of claim 9, wherein the excitation wavelengths for sorting collection and counting by the flow cytometer include 405nm and 488 nm.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105865988A (en) * | 2015-01-23 | 2016-08-17 | 中国科学院重庆绿色智能技术研究院 | Detection and analysis method for cell size distribution of phytoplankton |
| CN106018245A (en) * | 2016-05-20 | 2016-10-12 | 河海大学 | Method for enriching and detecting microcystis single cell and colony biomass |
-
2021
- 2021-07-14 CN CN202110795919.4A patent/CN114034625A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105865988A (en) * | 2015-01-23 | 2016-08-17 | 中国科学院重庆绿色智能技术研究院 | Detection and analysis method for cell size distribution of phytoplankton |
| CN106018245A (en) * | 2016-05-20 | 2016-10-12 | 河海大学 | Method for enriching and detecting microcystis single cell and colony biomass |
Non-Patent Citations (4)
| Title |
|---|
| 侍茂崇 等编: "《海洋调查方法》", 31 August 2016, 中国海洋大学出版社 * |
| 傅明珠 等: "南黄海浮游植物初级生产力粒级结构与碳流途径分析", 《海洋学报》 * |
| 吴忠兴: "我国微囊藻多样性分析及其种群优势的生理学机制研究", 《中国优秀博硕士学位论文全文数据库(博士) 工程科技Ⅰ辑》 * |
| 陈芸燕等: "基于浮游植物流式细胞仪对胶州湾春季浮游植物的研究", 《海洋科学》 * |
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