Summary of important research literatures on the single-cell transcriptome in March

The spermatogenesis process is a complex and rigorous process of differentiation, following the adult testis transcriptome study in Cell Research in October 18 [1] and the February 19 February Cell Reports on newborn and adulthood. The pace of identification of human testicular cells at the single cell level [2], this month in Nature Communications, researchers at the European Institute of Molecular Biology at the European Institute of Bioinformatics used 10x Genomics platform for mice during adult and juvenile development. The testes were sequenced and analyzed by single-cell transcriptome [3]. The study accurately classified cells at different stages of germ cell development, including rare somatic cells and spermatogonia. It is worth mentioning that in order to accurately capture cell types with low transcriptional activity, the researchers applied a new statistical tool, EmptyDrops, to identify fine-line and even-phase spermatocytes not found in previous studies. In addition, through the CUR and RUN experiments, the researchers analyzed the temporal dynamics of X chromosome reactivation after meiosis, and determined that a large number of chromatin remodeling occurred after meiosis. This study provides a new understanding of the complex processes and important events in the spermatogenesis process.

Figure 1 experimental design line

Adaptive immunity is based on the selection and expansion of antigen-specific T cells from a diverse pool of naive precursors, but our understanding of early adaptive immunity is limited. This month, in Nature Immunology, researchers from the Department of Immunohematology and Transfusion at the Leiden University Medical Center identified fetal intestines by flow cytometric sorting, 10x Genomics single-cell transcriptome and immune pool analysis. 22 CD4+ T cell types [4]. The results showed that Memory-like CD4+ T cells highly expressed Ki-67. Pathway analysis reveals a differentiation trajectory associated with cellular activation and pro-inflammatory effects. TCR lineage analysis revealed clonal expansion, interconnection between different lineage features and memory-like CD4+ T cell subsets. Flow cytometry showed that Memory-like CD4 + T cells co-localized with antigen presenting cells. This study provides evidence of the production of Memory-like CD4+ T cells in the human fetus.

Figure 2 Analysis of differentiation trajectories of fetal intestinal CD4 + T cells

(2) Exhausted CD8+ T cell subsets mediate tumor control and immune checkpoint blocking response

T cell dysfunction is a hallmark of many cancers, but the basis of T cell dysfunction and the mechanism by which inhibitory receptor PD-1 (anti-PD-1) antibodies block regenerating T cells is not fully understood. This month, in Nature Immunology, researchers from the Department of Pediatric Oncology at the Dana-Farber Cancer Institute used 10X Genomics single-cell transcriptome sequencing experiments to find that anti-PD-1 therapy acts on exhausted CD8+ T tumor-infiltrating lymphocytes (TIL). a specific subgroup [5]. Similar to chronic viral infections, dysfunctional CD8+ TIL has classical epigenetic and transcriptional depletion characteristics. Exhausted CD8+ T cell subsets, including "progenitor depletion" TILs, retain versatility and persist for long-term existence and differentiate into "final depletion" of TILs. Melanoma patients with a higher percentage of progenitor depleted cells have a longer response to immunotherapy. Therefore, expanding the progenitor depleted CD8 + T cell population may be an important method to improve the response to checkpoint blockade.

(3) Stem cell memory-like T cell production process tracking

The selective differentiation of Naive T cells into pluripotent T cells is clinically important for cell-based cancer immunotherapy. In this month's Nature Biotechnology, researchers from Stanford University used a modified dye dilution method to track the proliferation of Naive T cells [6]. Using 23 markers, the researchers defined a set of proteins that were largely controlled by division or time, and found that undivided cells accounted for the majority of phenotypically diverse cells. The researchers constructed a map of cell state changes during Naive T cell expansion. By examining the cellular signals on the spectrum, the researchers selected a BTK and ITK inhibitor, ibrutinib, to be treated prior to T cell differentiation to direct cell differentiation into T single cell memory (TSCM)-like phenotype cells. The cell fate tracking method of this study provides a viable tool for guiding cell differentiation.

Figure 3 T cell proliferation tracking method for cancer immunotherapy route

3. Single-cell transcriptome sequencing for cranial nerve research <br> The lateral hypothalamic region (LHA) coordinates a range of basic behaviors including sleep, wakefulness, feeding, stress and motivational behavior. In this month's "Nature Neuroscience," researchers from the Department of Physiology and Neurobiology at the University of Connecticut analyzed and identified different cell types in mouse LHA by 10X Genomics single-cell transcriptome sequencing [7]. The study defined a total of 15 different glutamatergic neuron populations and 15 GABAergic neurons, including known and new cell types. Using anatomical and behavioral methods, the researchers further identified a new neuronal population of somatostatin expression that was specifically expressed in congenital motor behavior. This study laid the foundation for a better understanding of LHA function.

Figure 4: Classification of tSNE in LHA ^Glut and LHA ^GABA neurons

4. Single-cell transcriptome sequencing emerges in the field of plants
Compared to single-cell studies in animals, the single-cell study of plants is difficult due to the cell wall characteristics of plants, but following the single-cell transcriptomics study of Arabidopsis root tissue in February [8], this month in "Developmental A single-cell transcriptomics study based on Arabidopsis root tissue was published in Cell [9]. Researchers from the Center for Plant Molecular Biology at the University of Tübingen in Germany used 10X Genomics single-cell transcriptome sequencing to identify Arabidopsis root tissue cells. This map provides detailed temporal and spatial information, identifying all major cell types, including rare cells in the quiescent center (QC cells), revealing key developmental regulators and downstream pathways in the process of cell fate transformation into unique cell shapes and functions. gene. Through quasi-time series analysis, the study depicts the fine-grained trajectories from cells from niche to differentiation and the major regulatory transcription factors. At about the same time, researchers at the University of Washington's Genomics Science Center also used Arabidopsis root tissue single-cell sequencing studies to publish Arabidopsis root map results in the plant's top journal, Plant Cell [10]. In this study, in addition to the root cell classification similar to the above two articles, heat stress treatment was used to reveal the heterogeneity of response within the cell under abiotic stress. This study of surface single-cell transcriptomics studies has broad prospects in plant development and physiology.

Figure 5 Gene change heat map from root cell to cell maturation process

5. Advances in single-cell research methods <br> In addition to the above research on single-cell transcriptomics, methodological studies based on single-cellomics analysis have also been published this month, including the Nature Method for cellular component analysis. CPM method [11], Nucleic Acids Research on cell-specific network construction based on single-cell transcriptome data [12], Genome Biology on low-transcription-level cell recognition based on droplet single-cell transcriptome data ( The development of the EmptyDrops software [13], the development of the Palantir algorithm for the identification of cell likelihoods in Nature Biotechnology [14] and the research on the optimization of tSNE algorithm in the Nature Method [15]. These studies have provided a powerful tool for the further development of future single-cellomics research.

In order to facilitate the single-cellomics research of domestic researchers, Shanghai Biochip Co., Ltd. (SBC) introduced the 10x Genomics single-cellomics detection platform in 2018, which provides an integrated solution from sample to analysis for researchers. We welcome all scientific research friends to carry out in-depth communication and extensive cooperation with us.

1. Guo, J., et al., The adult human testis transcriptional cell atlas. Cell Res, 2018. 28 (12): p. 1141-1157.
2. Sohni, A., et al., The Neonatal and Adult Human Testis Defined at the Single-Cell Level. Cell Rep, 2019. 26 (6): p. 1501-1517 e4.
3. Ernst, C., et al., Staged developmental mapping and X chromosome transcriptional dynamics during mouse spermatogenesis. Nat Commun, 2019. 10 (1): p. 1251.
4. Li, N., et al., Memory CD4(+) T cells are generated in the human fetal intestine. Nat Immunol, 2019. 20 (3): p. 301-312.
5. Miller, BC, et al., Subsets of exhausted CD8(+) T cells differentially mediate tumor control and respond to checkpoint blockade. Nat Immunol, 2019. 20 (3): p. 326-336.
6. Good, Z., et al., Proliferation tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells. Nat Biotechnol, 2019. 37 (3): p. 259-266.
7. Mickelsen, LE, et al., Single-cell transcriptomic analysis of the lateral hypothalamic area reveals molecularly distinct populations of inhibitory and excitatory neurons. Nat Neurosci, 2019. 22 (4): p. 642-656.
8. Ryu, KH, et al., Single-cell RNA sequencing resolves molecular relationships among individual plant cells. Plant Physiol, 2019.
9. Denyer, T., et al., Spatiotemporal Developmental Trajectories in the Arabidopsis Root Revealed Using High-Throughput Single-Cell RNA Sequencing. Dev Cell, 2019. 48 (6): p. 840-852 e5.
10. Jean-Baptiste, K., et al., Dynamics of gene expression in single root cells of A. thaliana. Plant Cell, 2019.
11. Frishberg, A., et al., Cell composition analysis of bulk genomics using single-cell data. Nat Methods, 2019.
12. Dai, H., et al., Cell-specific network constructed by single-cell RNA sequencing data. Nucleic Acids Res, 2019.
13. Lun, ATL, et al., EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol, 2019. 20 (1): p.
14. Setty, M., et al., Characterization of cell fate probabilities in single-cell data with Palantir. Nat Biotechnol, 2019.
15. Linderman, GC, et al., Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nat Methods, 2019. 16 (3): p. 243-245.