top of page

Review & news in single-cell/spatial omics, epigenetics & brain development)

​Single-cell/spatial omics:​​

  1. Costa, I. G.. (2023). Dissecting gene regulation with multimodal sequencing. Nature Methods. https://doi.org/10.1038/s41592-023-01957-1

  2. Lee, M. Y. Y., & Li, M.. (2023). Integration of multi-modal single-cell data. Nature Biotechnology. https://doi.org/10.1038/s41587-023-01826-4

  3. Preissl, S., Gaulton, K. J., & Ren, B.. (2023). Characterizing cis-regulatory elements using single-cell epigenomics. Nature Reviews Genetics, 24(1), 21–43. https://doi.org/10.1038/s41576-022-00509-1

  4. Polychronidou, et al. (2023). Single‐cell biology: what does the future hold?. Molecular Systems Biology, 19(7). https://doi.org/10.15252/msb.202311799

  5. Vandereyken, et al. (2023). Methods and applications for single-cell and spatial multi-omics. Nature Reviews Genetics, 24(8), 494–515. https://doi.org/10.1038/s41576-023-00580-2

  6. Baysoy, et al (2023). The technological landscape and applications of single-cell multi-omics. Nature reviews. Molecular cell biology, 1–19. https://doi.org/10.1038/s41580-023-00615-w

  7. Bressan, et al. (2023). The dawn of spatial omics. Science, 381(6657), eabq4964. https://doi.org/10.1126/science.abq4964

 

Epigenetics:

  1. Jambhekar, A., Dhall, A., & Shi, Y.. (2019). Roles and regulation of histone methylation in animal development. Nature Reviews Molecular Cell Biology, 20(10), 625–641. https://doi.org/10.1038/s41580-019-0151-1

  2. Michalak, E. M., Burr, M. L., Bannister, A. J., & Dawson, M. A.. (2019). The roles of DNA, RNA and histone methylation in ageing and cancer. Nature Reviews Molecular Cell Biology, 20(10), 573–589. https://doi.org/10.1038/s41580-019-0143-1

  3. Carter, B., & Zhao, K.. (2021). The epigenetic basis of cellular heterogeneity. Nature Reviews Genetics, 22(4), 235–250. https://doi.org/10.1038/s41576-020-00300-0

  4. Macrae, T. A., Fothergill-Robinson, J., & Ramalho-Santos, M.. (2023). Regulation, functions and transmission of bivalent chromatin during mammalian development. Nature Reviews Molecular Cell Biology, 24(1), 6–26. https://doi.org/10.1038/s41580-022-00518-2

 

Brain development:

  1. Valencia, A. M., & Pașca, S. P. (2022). Chromatin dynamics in human brain development and disease. Trends in cell biology, 32(2), 98–101. https://doi.org/10.1016/j.tcb.2021.09.001

  2. Zhou, Y., Song, H., & Ming, G. L. (2023). Genetics of human brain development. Nature reviews. Genetics, https://doi.org/10.1038/s41576-023-00626-5

​​

Benchmarking of single-cell analysis data

  1. scRNA-seq Doublet detection: Xi, N. M., & Li, J. J. (2021). Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data. Cell systems, 12(2), 176–194.e6. https://doi.org/10.1016/j.cels.2020.11.008

  2. scATAC-seq Doublet detection: Thibodeau, et al. (2021). AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data. Genome Biology, 22(1). https://doi.org/10.1186/s13059-021-02469-x

  3. Benchmarking of scCUT&Tag: Raimundo, et al. (2023). A benchmark of computational pipelines for single-cell histone modification data. Genome Biology, 24(1). https://doi.org/10.1186/s13059-023-02981-2

  4. Benchmarking of scATAC-seq: De Rop, et al. (2023). Systematic benchmarking of single-cell ATAC-sequencing protocols. Nature biotechnology, 10.1038/s41587-023-01881-x. Advance online publication. https://doi.org/10.1038/s41587-023-01881-x

  5. Integration benchmarking: Luecken, et al. (2022). Benchmarking atlas-level data integration in single-cell genomics. Nature Methods, 19(1), 41–50. https://doi.org/10.1038/s41592-021-01336-8

Useful single-cell (epigenetics) data analysis tools or packages

  1. ChromVar: Schep, et al. (2017). chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nature Methods, 14(10), 975–978. https://doi.org/10.1038/nmeth.4401

  2. RNA velocity: La Manno, et al. (2018). RNA velocity of single cells. Nature, 560(7719), 494–498. https://doi.org/10.1038/s41586-018-0414-6

    Bergen, et al. (2020). Generalizing RNA velocity to transient cell states through dynamical modeling. Nature biotechnology, 38(12), 1408–1414. https://doi.org/10.1038/s41587-020-0591-3

  3. CisTopic: Bravo González-Blas, et al. (2019). cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nature Methods, 16(5), 397–400. https://doi.org/10.1038/s41592-019-0367-1

  4. Harmony: Korsunsky, et al. (2019). Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods, 16(12), 1289–1296. https://doi.org/10.1038/s41592-019-0619-0

  5. scDblFinder: Germain, et al. (2021). Doublet identification in single-cell sequencing data using scDblFinder. F1000research, 10, 979. https://doi.org/10.12688/f1000research.73600.1

  6. Enhancer ABC model: Nasser, et al. (2021). Genome-wide enhancer maps link risk variants to disease genes. Nature, 593(7858), 238–243. https://doi.org/10.1038/s41586-021-03446-x

  7. Cromatin Velocity: Tedesco, et al. (2022). Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin. Nature biotechnology, 40(2), 235–244. https://doi.org/10.1038/s41587-021-01031-1

  8. scDRS: Zhang, et al. (2022). Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nature Genetics, 54(10), 1572–1580. https://doi.org/10.1038/s41588-022-01167-z

  9. sc-linker: Jagadeesh,et al. (2022). Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics. Nature Genetics, 54(10), 1479–1492. https://doi.org/10.1038/s41588-022-01187-9

  10. CellSpace: Tayyebi, et al. (2022). Scalable sequence-informed embedding of single-cell ATAC-seq data with CellSpace. https://doi.org/10.1101/2022.05.02.490310

  11. MIRA: Lynch, et al. (2022). MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells. Nature Methods, 19(9), 1097–1108. https://doi.org/10.1038/s41592-022-01595-z

  12. SCENIC+: Bravo González-Blas, et al. (2023). SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nature methods, 10.1038/s41592-023-01938-4. Advance online publication. https://doi.org/10.1038/s41592-023-01938-4

  13. Dictys: Wang, et al. (2023). Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics. Nature Methods. https://doi.org/10.1038/s41592-023-01971-3

  14. Tilted CCA: Lin, K. Z., & Zhang, N. R.. (2023). Quantifying common and distinct information in single-cell multimodal data with Tilted Canonical Correlation Analysis. Proceedings of the National Academy of Sciences, 120(32). https://doi.org/10.1073/pnas.2303647120

  15. Enhancer CIA model: Luo, et al. (2023). Dynamic network-guided CRISPRi screen identifies CTCF-loop-constrained nonlinear enhancer gene regulatory activity during cell state transitions. Nature Genetics. https://doi.org/10.1038/s41588-023-01450-7

  16. MultiVI: Ashuach, et al. (2023). MultiVI: deep generative model for the integration of multimodal data. Nature Methods, 20(8), 1222–1231. https://doi.org/10.1038/s41592-023-01909-9 ****

  17. SIMBA: Chen, et al. (2023). SIMBA: single-cell embedding along with features. Nature methods, 10.1038/s41592-023-01899-8. Advance online publication. https://doi.org/10.1038/s41592-023-01899-8

  18. SComatic: Muyas, et al. (2023). De novo detection of somatic mutations in high-throughput single-cell profiling data sets. Nature biotechnology, 10.1038/s41587-023-01863-z. Advance online publication. https://doi.org/10.1038/s41587-023-01863-z

  19. StabMap: Ghazanfar, S., Guibentif, C., & Marioni, J. C. (2023). Stabilized mosaic single-cell data integration using unshared features. Nature biotechnology, 10.1038/s41587-023-01766-z. Advance online publication. https://doi.org/10.1038/s41587-023-01766-z

  20. CZ cellxgene-census: https://chanzuckerberg.github.io/cellxgene-census/index.html#

Single-cell or spatial omics data analysis Python packages

  1. Scanpy: https://scanpy.readthedocs.io/en/stable/

    Wolf, F. A., Angerer, P., & Theis, F. J..(2018). SCANPY: large-scale single-cell gene expression data analysis. Genome Biology, 19(1). https://doi.org/10.1186/s13059-017-1382-0

  2. Muon: https://muon.readthedocs.io/en/latest/

    Bredikhin, D., Kats, I., & Stegle, O.. (2022). MUON: multimodal omics analysis framework. Genome Biology, 23(1). https://doi.org/10.1186/s13059-021-02577-8

  3. Squidpy: https://squidpy.readthedocs.io/en/stable/

    Palla,et al. (2022). Squidpy: a scalable framework for spatial omics analysis. Nature Methods, 19(2), 171–178. https://doi.org/10.1038/s41592-021-01358-2

  4. EpiScanpy: https://episcanpy.readthedocs.io/en/latest/

    Danese, et al.(2021). EpiScanpy: integrated single-cell epigenomic analysis. Nature Communication, 12, 5228 . https://doi.org/10.1038/s41467-021-25131-3

  5. SnapATAC: https://kzhang.org/SnapATAC2/index.html

    Fang, R. et al. (2021). Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nature Communications, 12, 1337. https://doi.org/10.1038/s41467-021-21583-9

  1. Seurat: https://satijalab.org/seurat/

    Stuart, et al. (2019). Comprehensive Integration of Single-Cell Data. Cell, 177(7), 1888–1902.e21. https://doi.org/10.1016/j.cell.2019.05.031

    Hao, et al. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573–3587.e29. https://doi.org/10.1016/j.cell.2021.04.048

    Hao, et al. (2023). Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology. https://doi.org/10.1038/s41587-023-01767-y

  2. Monocle3: https://cole-trapnell-lab.github.io/monocle3/docs/updates/

    Cao, et al. (2019). The single-cell transcriptional landscape of mammalian organogenesis. Nature, 566(7745), 496–502. https://doi.org/10.1038/s41586-019-0969-x

  3. Signac: https://stuartlab.org/signac/

    Stuart, et al. (2021). Single-cell chromatin state analysis with Signac. Nature Methods, 18(11), 1333–1341. https://doi.org/10.1038/s41592-021-01282-5

  4. ArchR: https://www.archrproject.com/

    Granja, et al. (2021). ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nature Genetics, 53(3), 403–411. https://doi.org/10.1038/s41588-021-00790-6

  5. Spatial Research: https://www.spatialresearch.org/resources-computational-tools/

Single-cell or spatial omics data analysis R packages

Spatial omics

  1. Spatial Transcriptomics: Ståhl, et al. (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 353(6294), 78–82. https://doi.org/10.1126/science.aaf2403

  2. sciMAP-ATAC: Thornton, et al(2021). Spatially mapped single-cell chromatin accessibility. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-21515-7

  3. Spatial-ATAC: Deng, et al. (2022). Spatial profiling of chromatin accessibility in mouse and human tissues. Nature, 609(7926), 375–383. https://doi.org/10.1038/s41586-022-05094-1

  4. Spatial-ATAC: Llorens-Bobadilla, et al. (2023). Solid-phase capture and profiling of open chromatin by spatial ATAC. Nature Biotechnology. https://doi.org/10.1038/s41587-022-01603-9

  5. Spatial CUT&Tag: Deng, et al. (2022). Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level. Science, 375(6581), 681–686. https://doi.org/10.1126/science.abg7216

  6. Spatial ATAC-RNA-seq or Spatial CUT&Tag-RNA-seq: Zhang, et al. (2023). Spatial epigenome-transcriptome co-profiling of mammalian tissues. Nature, 616(7955), 113–122. https://doi.org/10.1038/s41586-023-05795-1

Single-cell multi-omics

Refer to comparison tables in review papers:

  1. Vandereyken, et al. (2023). Methods and applications for single-cell and spatial multi-omics. Nature Reviews Genetics, 24(8), 494–515. https://doi.org/10.1038/s41576-023-00580-2

  2. Baysoy, et al (2023). The technological landscape and applications of single-cell multi-omics. Nature reviews. Molecular cell biology, 1–19. https://doi.org/10.1038/s41580-023-00615-w

Single-cell DNA methylation sequencing

  1. snmC-seq2: Luo, et al. (2018). Robust single-cell DNA methylome profiling with snmC-seq2. Nature communications, 9(1), 3824. https://doi.org/10.1038/s41467-018-06355-2

  2. Liu, et al. (2021). DNA methylation atlas of the mouse brain at single-cell resolution. Nature, 598(7879), 120–128. https://doi.org/10.1038/s41586-020-03182-8

  3. snmC-seq3 & snm3C-seq (DNA methylation and chromatin conformation): Liu, et al. (2023). Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain. https://doi.org/10.1101/2023.04.16.536509

  4. sciMET: Nichols, et al. (2022). High-throughput robust single-cell DNA methylation profiling with sciMETv2. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-35374-3

Bulk CUT&Tag and single-cell CUT&Tag

  1. CUT&Tag conceptulization: Kaya-Okur, et al. (2019). CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-09982-5

  2. scCUT&Tag on 10X Genomics Platform: Bartosovic, et al.(2021). Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nature Biotechnology, 39(7), 825–835. https://doi.org/10.1038/s41587-021-00869-9

  3. Wu, et al. (2021). Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nature Biotechnology, 39(7), 819–824. https://doi.org/10.1038/s41587-021-00865-z

  4. Gopalan, S., Wang, Y., Harper, N. W., Garber, M., & Fazzio, T. G.. (2021). Simultaneous profiling of multiple chromatin proteins in the same cells. Molecular Cell, 81(22), 4736–4746.e5. https://doi.org/10.1016/j.molcel.2021.09.019

  5. Janssens, et al. (2022). CUT&Tag2for1: a modified method for simultaneous profiling of the accessible and silenced regulome in single cells. Genome Biology, 23(1). https://doi.org/10.1186/s13059-022-02642-w

  6. Co-profile histone modification & cell surface proteins: Zhang, et al. (2022). Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro. Nature Biotechnology, 40(8), 1220–1230. https://doi.org/10.1038/s41587-022-01250-0

  7. Co-profile 2 histone modifications & chromatin accessibility: Bartosovic, M., & Castelo-Branco, G.. (2023). Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag. Nature Biotechnology, 41(6), 794–805. https://doi.org/10.1038/s41587-022-01535-4

  8. Co-profile cell surface proteins and 2 histone modifications: Stuart, et al. (2023). Nanobody-tethered transposition enables multifactorial chromatin profiling at single-cell resolution. Nature Biotechnology, 41(6), 806–812. https://doi.org/10.1038/s41587-022-01588-5

  9. Henikoff, et al. (2023). Epigenomic analysis of Formalin-Fixed Paraffin-Embedded samples by CUT&Tag. https://doi.org/10.1101/2023.06.20.545743

Single-cell ATAC sequencing methods 

  1. Droplet-based: Buenrostro, et al. (2015). Single-cell chromatin accessibility reveals principles of regulatory variation. Nature, 523(7561), 486–490. https://doi.org/10.1038/nature14590

  2. Cellular indexing based: Cusanovich, et al. (2015). Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science, 348(6237), 910–914. https://doi.org/10.1126/science.aab1601

  3. s3-ATAC: Mulqueen, et al. (2021). High-content single-cell combinatorial indexing. Nature Biotechnology, 39(12), 1574–1580. https://doi.org/10.1038/s41587-021-00962-z

  4. Comparison of scATAC-seq: De Rop, et al. (2023). Systematic benchmarking of single-cell ATAC-sequencing protocols. Nature biotechnology, 10.1038/s41587-023-01881-x. Advance online publication. https://doi.org/10.1038/s41587-023-01881-x

Single cell RNA sequencing methods

​

  1. Smart-seq2: Picelli, et al. (2013). Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nature Methods, 10(11), 1096–1098. https://doi.org/10.1038/nmeth.2639

  2. Droplet-based: Evan, et al. (2015). Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell, 161(5), 1202–1214. https://doi.org/10.1016/j.cell.2015.05.002

  3. Smart-seq3 (plate based): Hagemann-Jensen, et al. (2020). Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nature Biotechnology, 38(6), 708–714. https://doi.org/10.1038/s41587-020-0497-0.

  4. Smart-seq3xpress: Hagemann-Jensen, et al. (2022). Scalable single-cell RNA sequencing from full transcripts with Smart-seq3xpress. Nature Biotechnology, 40(10), 1452–1457. https://doi.org/10.1038/s41587-022-01311-4

  5. 10X Genomics Chromium: Zheng, et al. (2017). Massively parallel digital transcriptional profiling of single cells. Nature Communications, 8(1), 14049. https://doi.org/10.1038/ncomms14049

  6. Sci-RNA-seq: Cao, et al. (2017). Comprehensive single-cell transcriptional profiling of a multicellular organism. Science, 357(6352), 661–667. https://doi.org/10.1126/science.aam8940

  7. Comparison of scRNA-seq: Ziegenhain, et al. (2017). Comparative Analysis of Single-Cell RNA Sequencing Methods. Molecular Cell, 65(4), 631–643.e4. https://doi.org/10.1016/j.molcel.2017.01.023

Other useful bioinformatics resources

  1. WARP Pipelines: https://broadinstitute.github.io/warp/

  2. Introduction to Bioinformatics and Computational Biology: https://liulab-dfci.github.io/bioinfo-combio/

bottom of page