Comparative Transcriptomic Analyses of Thymocytes Using 10x Genomics and Parse ScRNA-seq Technologies

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2024 Nov 11

PMID 39528918

Authors

Igor Filippov

Chinna Susan Philip

Leif Schauser

Part Peterson

Affiliations

Soon will be listed here.

Abstract

Background: Single-cell RNA sequencing experiments commonly use 10x Genomics (10x) kits due to their high-throughput capacity and standardized protocols. Recently, Parse Biosciences (Parse) introduced an alternative technology that uses multiple in-situ barcoding rounds within standard 96-well plates. Parse enables the analysis of more cells from multiple samples in a single run without the need for additional reagents or specialized microfluidics equipment. To evaluate the performance of both platforms, we conducted a benchmark study using biological and technical replicates of mouse thymus as a complex immune tissue.

Results: We found that Parse detected nearly twice the number of genes compared to 10x, with each platform detecting a distinct set of genes. The comparison of multiplexed samples generated from 10x and Parse techniques showed 10x data to have lower technical variability and more precise annotation of biological states in the thymus compared to Parse.

Conclusion: Our results provide a comprehensive comparison of the suitability of both single-cell platforms for immunological studies.

References

Luecken M, Buttner M, Chaichoompu K, Danese A, Interlandi M, Mueller M . Benchmarking atlas-level data integration in single-cell genomics. Nat Methods. 2021; 19(1):41-50. PMC: 8748196. DOI: 10.1038/s41592-021-01336-8. View

La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V . RNA velocity of single cells. Nature. 2018; 560(7719):494-498. PMC: 6130801. DOI: 10.1038/s41586-018-0414-6. View

Xin Z, Lin M, Hao Z, Chen D, Chen Y, Chen X . The immune landscape of human thymic epithelial tumors. Nat Commun. 2022; 13(1):5463. PMC: 9482639. DOI: 10.1038/s41467-022-33170-7. View

Klein F, Veiga-Villauriz C, Borsch A, Maio S, Palmer S, Dhalla F . Combined multidimensional single-cell protein and RNA profiling dissects the cellular and functional heterogeneity of thymic epithelial cells. Nat Commun. 2023; 14(1):4071. PMC: 10333192. DOI: 10.1038/s41467-023-39722-9. View

Brown D, Anttila C, Ling L, Grave P, Baldwin T, Munnings R . A risk-reward examination of sample multiplexing reagents for single cell RNA-Seq. Genomics. 2024; 116(2):110793. DOI: 10.1016/j.ygeno.2024.110793. View

Lee M, Lee E, Han S, Choi Y, Kwon D, Choi H . Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells. Nat Commun. 2020; 11(1):4367. PMC: 7459300. DOI: 10.1038/s41467-020-18155-8. View

Kernfeld E, Genga R, Neherin K, Magaletta M, Xu P, Maehr R . A Single-Cell Transcriptomic Atlas of Thymus Organogenesis Resolves Cell Types and Developmental Maturation. Immunity. 2018; 48(6):1258-1270.e6. PMC: 6013397. DOI: 10.1016/j.immuni.2018.04.015. View

Arun G, Aggarwal D, Spector D . Long Non-Coding RNA: Functional Implications. Noncoding RNA. 2020; 6(2). PMC: 7344863. DOI: 10.3390/ncrna6020022. View

Hao Y, Hao S, Andersen-Nissen E, Mauck 3rd W, Zheng S, Butler A . Integrated analysis of multimodal single-cell data. Cell. 2021; 184(13):3573-3587.e29. PMC: 8238499. DOI: 10.1016/j.cell.2021.04.048. View

10.

Kharchenko P, Silberstein L, Scadden D . Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014; 11(7):740-2. PMC: 4112276. DOI: 10.1038/nmeth.2967. View

11.

Ahlmann-Eltze C, Huber W . glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data. Bioinformatics. 2020; 36(24):5701-5702. PMC: 8023675. DOI: 10.1093/bioinformatics/btaa1009. View

12.

Haque A, Engel J, Teichmann S, Lonnberg T . A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 2017; 9(1):75. PMC: 5561556. DOI: 10.1186/s13073-017-0467-4. View

13.

Zeng Y, Liu C, Gong Y, Bai Z, Hou S, He J . Single-Cell RNA Sequencing Resolves Spatiotemporal Development of Pre-thymic Lymphoid Progenitors and Thymus Organogenesis in Human Embryos. Immunity. 2019; 51(5):930-948.e6. DOI: 10.1016/j.immuni.2019.09.008. View

14.

Muskovic W, Powell J . DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data. Genome Biol. 2021; 22(1):329. PMC: 8641258. DOI: 10.1186/s13059-021-02547-0. View

15.

Li Y, Li K, Zhu L, Li B, Zong D, Cai P . Development of double-positive thymocytes at single-cell resolution. Genome Med. 2021; 13(1):49. PMC: 8004397. DOI: 10.1186/s13073-021-00861-7. View

16.

Kousa A, Jahn L, Zhao K, Flores A, Acenas 2nd D, Lederer E . Age-related epithelial defects limit thymic function and regeneration. Nat Immunol. 2024; 25(9):1593-1606. PMC: 11362016. DOI: 10.1038/s41590-024-01915-9. View

17.

Steier Z, Aylard D, McIntyre L, Baldwin I, Kim E, Lutes L . Single-cell multiomic analysis of thymocyte development reveals drivers of CD4 T cell and CD8 T cell lineage commitment. Nat Immunol. 2023; 24(9):1579-1590. PMC: 10457207. DOI: 10.1038/s41590-023-01584-0. View

18.

Yamawaki T, Lu D, Ellwanger D, Bhatt D, Manzanillo P, Arias V . Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling. BMC Genomics. 2021; 22(1):66. PMC: 7818754. DOI: 10.1186/s12864-020-07358-4. View

19.

Ashby K, Hogquist K . A guide to thymic selection of T cells. Nat Rev Immunol. 2023; 24(2):103-117. DOI: 10.1038/s41577-023-00911-8. View

20.

Wang X, He Y, Zhang Q, Ren X, Zhang Z . Direct Comparative Analyses of 10X Genomics Chromium and Smart-seq2. Genomics Proteomics Bioinformatics. 2021; 19(2):253-266. PMC: 8602399. DOI: 10.1016/j.gpb.2020.02.005. View