Data Fig. 3a-c, Supplementary Table 3). An interactive data browser for the atlases is publicly available (see 'Data availability'). Notably, our bulk RNA-seq and single-cell data were highly concordant, which indicates that our single-cell analytical approach did not introduce technical bias (Extended Data Fig. 3d).

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Background DNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBSs) in regulatory regions through footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq. Results Here, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite the differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impact the discrimination of footprint from the background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints. Conclusions We demonstrate that the impact of bias correction on footprinting performance is greater for DNase-seq than for ATAC-seq and that DNase-seq footprinting leads to better performance. It is possible to infer concordant footprints by using replicates, highlighting the importance of reproducibility assessment. The results presented here provide an overview of the advantages and limitations of footprinting analyses using ATAC-seq and DNase-seq. Electronic supplementary material The online version of this article (10.1186/s13059-019-1654-y) contains supplementary material, which is available to authorized users.

Fibroblasts are non-hematopoietic structural cells that define the architecture of organs, support the homeostasis of tissue-resident cells and play key roles in fibrosis, cancer, autoimmunity and wound healing. Recent studies have described fibroblast heterogeneity within individual tissues. However, the field lacks a definition of fibroblasts at single-cell resolution across tissues in healthy and diseased organs. Here, we integrated single-cell RNA transcriptomic data from ~150,000 fibroblast cells derived from 16 steady- and 11 perturbed-state mouse organs into fibroblast atlases. These data revealed two universal fibroblast cell subtypes, marked by expression of Pi16 or Col15a1, in all tissues; it also revealed discrete subsets of five specialized fibroblast subtypes in steady-state tissues and three activated fibroblast subtypes in perturbed or diseased tissues. These subsets were transcriptionally shaped by microenvironmental context rather than tissue-type alone. Inference of fibroblast lineage structure from the murine steady-state and perturbed-state fibroblast atlases suggested that specialized and activated subtypes are developmentally related to universal tissue-resident fibroblasts. Analysis of human samples revealed that fibroblast subtypes found in mice are conserved between species, including universal fibroblasts and activated phenotypes associated with pathogenicity in human cancer, fibrosis, arthritis and inflammation. In sum, a cross-species and pan-tissue approach to transcriptomics at single-cell resolution enabled us to define the organizing principles of the fibroblast lineage in health and disease.

DNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBS) in regulatory regions via footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq. Here, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impacts the discrimination of footprint from background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints.