scRNA-seq - providing new ideas for drug discovery

Introduction At present, various sequencing technologies have become powerful tools in the drug development process
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Single-cell RNA sequencing (scRNA-seq), simply understood literally, is a technology for sequencing and analyzing the transcriptome at the single-cell level
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At present, this technology has been widely used, and has been rapidly developed in the application, becoming a powerful tool for us to study cell types, disease processes and other issues
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PART.
01 Why use scRNA-seq? Traditional bulk RNA sequencing has an unavoidable problem: cellular heterogeneity
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As we all know, for multicellular organisms, although they develop from the same fertilized egg, the gene expression between different cells must be different.
These differences lead to the differentiation of cells, so that different cells undertake different physiological functions.

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Roughly speaking, the cellular composition of different organs and tissues differs, but what makes the analysis tricky is that even morphologically indistinguishable cells are highly heterogeneous in terms of gene expression levels and responses to stimuli.
This heterogeneity plays an important role in tissue biology and disease development.
For example, expression heterogeneity between pathogen cells or cancer cells may be relevant to human disease.
In drug development, cellular heterogeneity Sexuality also often causes problems such as difficulty in finding targets and drug resistance
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Figure 1.
Tumor Heterogeneity (Nature 2013 Vol.
501 Issue 7467 Pages 338-345) When using multicellular level analysis, information about the heterogeneity of each cell can easily be masked in the average information of multiple cells
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For example, the very familiar Western blot
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When we used Western blot to measure the amount of the protein, we could not distinguish whether the target protein was strongly expressed in 10% of cells, moderately expressed in 50% of cells, or weakly expressed in all cells
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But if we use flow cytometry, we can clearly distinguish the above
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Figure 2.
Western blot VS flow cytometry (https://zhuanlan.
zhihu.
com/p/28844468) The same is true for single-cell transcriptome sequencing
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When using bulk RNA-seq, per-cell transcriptome differences are also masked by the averaged information across multiple cells, whereas if each cell is analyzed at the single-cell level, we can obtain information about heterogeneity, which in turn Cells can be more accurately grouped, new cell types can be discovered, expression of random genes can be studied, and cell lineage pathways can be explored
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Provide more accurate information for drug research and development, open up new paths, and realize the real "prescribed medicine"
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Figure 3.
Single-cell measurements preserve a large amount of key information missing from genome analysis (Genome Research 2015 Vol.
25 Issue 10 Pages 1491-1498) Since the first single-cell transcriptome sequencing technology was developed by Tang et al.
in 2009, Today, dozens of different single-cell transcriptome sequencing technologies have been developed, and they each have different advantages and disadvantages.
After understanding the advantages and disadvantages of different technologies, we can choose the most suitable technology for our own research.

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Figure 4.
Development history of scRNA-seq.
Figure 5.
Some commonly used scRNA-seq (Chen, G.
, et al.
(2019).
"Single-Cell RNA-Seq Technologies and Related Computational data Analysis.
" Frontiers in Genetics 10( 317).
) PART.
02 Principles of single-cell RNA sequencing technology The basic process of single-cell sequencing is usually divided into the following parts: single-cell isolation, gene amplification, construction of information library, high-throughput sequencing, and data analysis
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There are many types of single-cell isolation techniques.
After single-cell isolation and dissolution, pg-level nucleic acids are obtained, amplified to ng or μg-level, and then used for subsequent sequencing
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At present, the commonly used single-cell isolation methods include limiting dilution method, micromanipulation (manual cell collection), laser capture microdissection, fluorescence-activated cell sorting, magnetic-activated cell sorting, and microfluidic technology
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Figure 6.
Single-cell sequencing process (Li Bo, Duo Hongrui.
Research progress on single-cell RNA sequencing data analysis methods [J].
Journal of Chongqing Normal University (Natural Science Edition), 2021,38(05):129-135+142 .
) At present, the commonly used single-cell RNA sequencing technologies include Tang RNA-seq, Smart-seq, Smart-seq 2, CEL-seq, Quartz-seq, STRT-seq, dro-seq, etc.
The sequencing platforms used include 10Genomics platform, Illumina®Bio-Rad® platform, BD Rhapsody™ platform, ICELL8 platform and C1™ single-cell automated preparation system,
etc.
Next, the 10Genomics platform is used as a representative to introduce the specific process of gene amplification and information library construction in the single-cell RNA sequencing process.

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10Genomics The 10×Genomics platform is a droplet-based nucleic acid barcode distribution system that provides millions of droplets carrying unique DNA barcodes, by microfluidic technology, first to gel beads with barcodes and primers Encapsulated in oil droplets (GEMs) with single cells
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Gel beads are lysed, cells are lysed to release mRNA, and barcoded and UMI-informed cDNA is generated by reverse transcription for sequencing
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The droplet oil layer is broken, the cDNA is amplified, the cDNA library is prepared, and then the library is sequenced using the Illumina sequencing platform to obtain a large number of single-cell gene expression and immune library data
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Figure 7.
Flow chart of the 10Genomics platform (https://zhuanlan.
zhihu.
com/p/269461589) The core of the 10Genomics technology is oil-encapsulated gel beads (GEMs)
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GEM (10X Genomics Gel in Emulsion) is a droplet reaction system composed of mRNA, magnetic beads, and emulsion.
Subsequent cell sorting and reverse transcription reactions are carried out in this droplet reaction system
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The structure of GEMs ensures that >90% of the reaction products in the oil droplets can be successfully labeled with unique barcode molecules
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Gel beads consist of gel beads and a primer on the magnetic beads
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The primer sequence ligated on the Gel bead consists of four parts: Illumina TruSeq Read 1 sequencing primer, 16 nt barcode (Barcode), 12 nt UMI and 30 nt Poly(dT) reverse transcription primer Figure 8.
Schematic diagram of GEM (left ), the schematic diagram of Gel bead (right) The specific process is to first prepare the sample into a single cell suspension, and then perform cell quality inspection and then carry out cell count and cell viability determination.
Finally, the cell viability is higher than 90%, and the cell concentration is 700-1200 cells /μL
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Take 75 μL of Master Mix+ cell suspension, 40 μL of gel beads containing barcode information and 280 μL of oil droplets into different chambers of Chromium Chip B, respectively, and form oil droplet-coated gel beads ( Gel Bead-In-EMulsions, GEMs)
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Effective GEMs contain gel beads, single cells, Master Mix, and oil
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Collect GEMs and mix them all, gel beads automatically dissolve in each oil droplet to release a large number of Barcode primer sequences, cells are lysed to release mRNA, mRNA and reverse transcriptase, poly dT reverse transcription primers on gel beads, and dNTPs When the substrates come into contact with each other, a reverse transcription reaction occurs under the action of reverse transcriptase to generate a cDNA strand with Barcode and UMI information for sequencing, and then SMART amplification method is used to complete the second strand synthesis
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The cNDA was fragmented and the oil droplets were broken, and a strand of cDNA was purified by magnetic beads, and then PCR amplification was performed using the cDNA as a template
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After the cDNA amplification is completed, the cDNA is first broken into fragments of about 200-300 bp by chemical methods, and the cDNA fragments are screened by end repair and A addition.
The P7 adaptor adapter is connected to the Read2 sequencing primer, and the sample Index is introduced by PCR amplification.
Finally, Fragment screening was performed to construct a cDNA library containing P5 and P7 linkers
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After the library is completed, the library inspection is performed, and after the cDNA library library inspection is qualified, the sequencer is directly sequenced on the Illumina sequencer
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After sequencing was completed, data analysis was performed
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Data analysis The data obtained by single-cell RNA sequencing are usually carried out from the following aspects: 1.
Sequence alignment and expression level quantification; 2.
Data preprocessing; 3.
Data standardization; 4.
Selection of highly variable genes and dimensionality reduction analysis 5.
Cluster analysis; 6.
Cell type annotation; 7.
Identification and enrichment analysis of differential expression; 8.
Advanced analysis: such as cell lineage tracing, biological network construction, spatial transcriptome, etc.

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Figure 9.
Data analysis of scRNA-seq (Li Bo, Duo Hongrui.
Research progress of single-cell RNA sequencing data analysis methods [J].
Journal of Chongqing Normal University (Natural Science Edition), 2021, 38(05): 129-135 +142.
) PART.
03 The advantages and disadvantages of single-cell sequencing compared with some current mainstream technologies The first disadvantage of single-cell sequencing is that the sample preparation and library construction costs are higher, the data volume is larger, more complex, and requires more time , equipment, capacity to carry out the work
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The second defect of single-cell sequencing is the biological error across cells, because there are some biological variations between sample cells, mainly including: 1.
Transcriptional pulse, which means that not all gene transcription is in the initiation stage , mainly by the capture time to determine whether these genes are on or off; 2.
Then the processing speed of each RNA is different; 3.
Environmental stimuli can also affect gene expression; 4.
Timing changes, referring to cell cycle Such cellular timing changes can affect the gene expression profile of individual cells
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The third defect of single-cell sequencing is the technical differences between samples, which mainly include: 1.
Different cell-specific capture efficiencies, and different cells capture different transcripts, resulting in different sequencing depths; 2.
Library quality: the obtained samples will The presence of degraded RNA, low-viability cells, a large amount of free-floating RNA, and inaccurate cell quantification lead to lower quality indicators; 3.
Amplification bias: During the amplification step of library preparation, not all transcripts are amplified to The same level; 4.
Batch effect: It can be seen from the figure that there are significant differences in cell expression due to different batches, which requires that all RNA isolation and construction should be completed on the same day and by the same person in the same place in the experiment.
The library works to try to avoid batch effects
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The two mainstream technologies currently used in single-cell sequencing are 10x Genomics and smart-seq, and they have their own advantages and disadvantages
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The first is the general steps of smart-seq: cells are made into single-cell suspension, RNA reverse transcription with poly (A) tail, template conversion, cDNA amplification, cDNA fragmentation, addition of library PCR primers for amplification, and sequencing
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Its main advantage lies in the fact that the M-MLV reverse transcriptase used in this technology is more inclined to select full-length cDNAs as substrates for its terminal transferase activity compared to truncated cDNAs
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As a result, its sequencing depth is greatly increased, and virtually all exons of each transcript can be detected, which enables it to be used to detect alternative splicing, and to perform comprehensive mutational analysis at the transcript level, expanding its Scope of application
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In addition, its advantages include: 1.
After technical improvements, Smart-seq2 can produce higher-quality libraries using off-the-shelf reagents, and the cost is also reduced, providing the possibility to analyze a large number of cells
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2.
The program components and principles of Smart-seq2 are public, allowing researchers to further improve it.
At present, many new results of single-cell sequencing have emerged on the basis of this program
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Its shortcomings are: the library building process relies on well plates, which makes it difficult for them to achieve high throughput, and smart-seq is based on 96-well plates, so only 96 cells can be measured in the same batch
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Microwells increase cell numbers by reducing pore volume, but are still limited
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Another drawback is that the primers are designed to selectively recognize only polyadenylated RNAs, so RNAs without poly(A) cannot be analyzed
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Next is 10x Genomics, which is droplet-dependent.
The general steps are to encapsulate a single cell and an RNA-capturing microbead in a droplet through microfluidic technology.
The bead-linked capture mRNA already contains UMI and Cell barcode information, so that each droplet carries unique information, after the enrichment of mRNA in the droplet, the beads are combined to complete the sequencing
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Its advantages are: 1.
In this method, the oil droplets containing cells account for about 5% of all oil droplets, which can better ensure that only one cell is contained in the cell-containing droplets
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2.
Each droplet contains a unique cell barcode and UMI, which can distinguish single cells
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3.
And it is not limited to the well plate, and can process more cells in the same batch in a short time
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Its shortcomings are: 1.
The capture efficiency of microbeads for mRNA is limited.
While achieving a breakthrough in the number of cells, the number of cell genes that can be measured is small, that is, the sequencing depth is relatively low
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2.
In addition, the sequencing of this technology is mostly 3'-end sequencing, and the measured sequence identity is very high, and the sensitivity and accuracy are not as good as full-field sequencing
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PART.
04 Conclusion scRNA-seq provides a transcriptome-level solution to the problem of heterogeneity.
Since its inception, it has developed rapidly and has been widely used, from gene expression to cell To pedigree analysis, scRNA-seq connects the dots, providing more directions for drug discovery today
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scRNA-seq shines today, and the future is just as exciting
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About the author Drug-seeking truth group They are students of Nanjing University's new drug research and development strategy course.
This article is completed by Du Yixiang, Xu Qian, Xu Siqiong, and Li Fei in the course
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They are full of vigor, their fighting spirit is high, they are down-to-earth to learn the knowledge of new drug research and development, and they firmly believe that I love my teacher and I love the truth more
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They are the successors of the new generation of pharmaceutical industry, and they are the future light in the field of new drug research and development! References: [1] Burrell, RA, et al.
(2013).
"The causes and consequences of genetic heterogeneity in cancer evolution.
" Nature 501(7467): 338-345.
[2] Chen, G.
, et al (2019).
"Single-Cell RNA-Seq Technologies and Related Computational data Analysis.
" Frontiers in Genetics 10(317).
[3] Potter, SS (2018).
"Single-cell RNA sequencing for the study of development, physiology and disease.
" Nature Reviews Nephrology 14(8): 479-492.
[4] Picelli, S.
(2017).
"Single-cell RNA-sequencing: The future of genome biology is now.
" RNA Biology 14(5 ): 637-650.
[5] Trapnell, C.
(2015).
"Defining cell types and states with single-cell genomics.
" Genome Research 25(10): 1491-1498.
[6] Li Bo, Duo Hongrui .
Research progress of single-cell RNA sequencing data analysis methods[J].