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Scalable co-sequencing of RNA and DNA from individual nuclei

Nature Methods volume 22, pages 477–487 (2025)Cite this article

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Abstract

The ideal technology for directly investigating the relationship between genotype and phenotype would analyze both RNA and DNA genome-wide and with single-cell resolution; however, existing tools lack the throughput required for comprehensive analysis of complex tumors and tissues. We introduce a highly scalable method for jointly profiling DNA and expression following nucleosome depletion (DEFND-seq). In DEFND-seq, nuclei are nucleosome-depleted, tagmented and separated into individual droplets for messenger RNA and genomic DNA barcoding. Once nuclei have been depleted of nucleosomes, subsequent steps can be performed using the widely available 10x Genomics droplet microfluidic technology and commercial kits. We demonstrate the production of high-complexity mRNA and gDNA sequencing libraries from thousands of individual nuclei from cell lines, fresh and archived surgical specimens for associating gene expression with both copy number and single-nucleotide variants.

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Fig. 1: Schematic and characterization of nucleosome-depletion methods.
Fig. 2: DEFND-seq.
Fig. 3: Copy number variation of DEFND-seq BJ fibroblasts.
Fig. 4: DEFND-seq applied to GBM tumor sample.
Fig. 5: Focal amplifications within GBM tumor cells.
Fig. 6: SNP and SNV detection.

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Data availability

The sequencing data and count matrices reported in this paper are available in the Gene Expression Omnibus (GSE224149). Fragment files for GBM tumors are stored on Zenodo at https://doi.org/10.5281/zenodo.13984672 (ref. 75). The COSMIC database (https://cancer.sanger.ac.uk/cosmic) was leveraged for SNP and SNV calling. Data from the sci-L3 assay were downloaded from the Sequence Read Archive project (PRJNA511715). Bulk BJ WGS data were acquired from GSE63577 (SRR1660537) and 10x ATAC PBMC data were acquired from 10x Genomics17. Source data are provided with this paper.

Code availability

All source code is available on the Sims Laboratory GitHub repository at https://github.com/simslab/, including code for generating expression count matrices (DropSeqPipeline8), clustering and differential expression (cluster_diffex2018) and genomic DNA analysis (dna10x).

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Acknowledgements

Some of the research reported here was performed in the Sulzberger Columbia Genome Center, which is supported by National Institutes of Health (NIH)/National Cancer Institute (NCI) grant P30CA013696 and NIH/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grant P30DK132710. P.A.S. and S.Z. were supported by a RISE grant from Columbia University. P.A.S. was supported by NIH/NCI grant U54CA274506. P.A.S., P.C., and J.N.B. were supported by NIH/National Institute of Neurological Disorders and Stroke (NINDS) grant R01NS103473. S.Z. was supported by NIH/NCI grant R01CA275184. P.T. was supported by the I.I. Rabi Scholars Program of Columbia University.

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Authors and Affiliations

  1. Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA

    Timothy R. Olsen, Pranay Talla, Romella K. Sagatelian & Peter A. Sims

  2. Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY, USA

    Julia Furnari & Jeffrey N. Bruce

  3. Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, NY, USA

    Peter Canoll & Shan Zha

  4. Institute for Cancer Genetics, Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA

    Shan Zha

  5. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA

    Peter A. Sims

  6. Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA

    Peter A. Sims

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Contributions

T.R.O., S.Z. and P.A.S. conceived experiments. T.R.O., P.T., R.K.S. and P.A.S. analyzed data. T.R.O. and P.T. performed the experiments. J.F. managed biobanked samples. P.C. and J.N.B. procured and characterized specimens. T.R.O. and P.A.S. wrote the manuscript. All authors reviewed and edited the manuscript.

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Correspondence to Peter A. Sims.

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P.A.S. receives patent royalties from Guardant Health. The other authors declare no competing interests.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Lei Tang and Hui Hua, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Copy number variation heatmap of freshly resected GBM tumor cells. Cells are grouped by gDNA cluster.

Chromosome location reference on bottom margin.

Extended Data Fig. 2 DEFND-seq applied to cryopreserved GBM tumor sample.

(a) Clustering of GBM cells by gene expression. (b) Same as (a) but colored by expression of MOBP. (c) Highly expressed genes for each cluster in (a). (d) log fold change of PTEN in the genome of the clusters in (a) showing deletions in clusters 1–6. Bars indicate the medians of each cluster. (e) log fold change of MYCN in the genome of clusters in (a) with amplifications in clusters 1–6. (f) Same as (e) but with CDK4. (g) – (i) Cells from (a) colored by genomic log fold change in (g) PTEN, (h) MYCN, and (i) CDK4. (j) – (l) Cells from (a) colored by log-normalized transcript counts for (j) PTEN, (k) MYCN, and (l) CDK4. (m) Pileup of every base along selected genomic regions for all transformed cells. PTEN, MYCN, and CDK4 regions are presented along with a selection of genes located in the sampled window.

Extended Data Fig. 3 Copy number variation heatmap of cryopreserved GBM tumor cells.

Cells are grouped by gene expression cluster. Chromosome location reference on bottom margin.

Extended Data Fig. 4 SNP and SNV detection.

(a) Sequencing saturation analysis of the number of unique fragments per nucleus for a GBM DEFND-seq library sequenced on an Illumina NovaSeq 6000, and on an Element Aviti. (b) Sequencing saturation analysis of the transcription start site enrichment for a library sequenced on both NovaSeq and Aviti sequencers. (c) Insert size distribution for the GBM library sequenced on NovaSeq and Aviti sequencers. (d) PREX1 genomic mutation detection projected on a UMAP derived from the gene expression dataset. (e) Same as (d) but colored by PREX1 expression. (f) Average frequency of PREX1 variant allele and average expression of PREX1 in astrocyte-like, proliferating, and proneural clusters. (g) Integrated genomic viewer (IGV) style plots of the transformed cells showing exonic regions of PREX1, coverage, and the location of the SNV49. The SNV is magnified to show the missense mutation and affected amino acid. Individual reads are shown for both the NovaSeq and Aviti sequencers and each read is colored by cell of origin (cell barcode). (h) Same as (g) but for the p.P72R mutation on TP53.

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Olsen, T.R., Talla, P., Sagatelian, R.K. et al. Scalable co-sequencing of RNA and DNA from individual nuclei. Nat Methods 22, 477–487 (2025). https://doi.org/10.1038/s41592-024-02579-x

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