The human body at cellular resolution: the NIH Human Biomolecular Atlas Program

Snyder, M; Lin, Shin; Posgai, Amanda L.; Atkinson, Mark A.; Regev, Aviv; Rood, Jennifer; Rosen, Orit; Gaffney, Leslie; Hupalowska, Anna; Satija, Rahul; Gehlenborg, Nils; Shendure, Jay; Laskin, Julia; Harbury, Pehr B.; Nystrom, Nicholas A.; Bar‐Joseph, Ziv; Zhang, Kun; Börner, Katy; Lin, Yiing; Conroy, Richard; Procaccini, Dena; Roy, Ananda L.; Pillai, Ajay S.; Brown, Marishka K.; Galis, Zorina S.

doi:10.1038/s41586-019-1629-x

Cited by 469 publications

(196 citation statements)

References 46 publications

Supporting

Mentioning

196

Contrasting

Order By: Relevance

“…To face such a complicated problem such as the spatial localization of cells from methodological, experimental, computational, and economic points of view it was launched a National Institutes of Health (NIH) initiative named Human Biomolecular Atlas Program (HuBMAP). It intends to develop a workflow for mapping the human body at single-cell resolution supporting technology development, data acquisition, and detailed spatial mapping [224]. A possible difficulty in these types of projects is determining the number of cells required to avoid losing information from very rare cells present in the tissue of interest.…”

Section: Discussionmentioning

confidence: 99%

A Single Cell but Many Different Transcripts: A Journey into the World of Long Non-Coding RNAs

Alessio

Bonadio

Buson

et al. 2020

IJMS

View full text Add to dashboard Cite

In late 2012 it was evidenced that most of the human genome is transcribed but only a small percentage of the transcripts are translated. This observation supported the importance of non-coding RNAs and it was confirmed in several organisms. The most abundant non-translated transcripts are long non-coding RNAs (lncRNAs). In contrast to protein-coding RNAs, they show a more cell-specific expression. To understand the function of lncRNAs, it is fundamental to investigate in which cells they are preferentially expressed and to detect their subcellular localization. Recent improvements of techniques that localize single RNA molecules in tissues like single-cell RNA sequencing and fluorescence amplification methods have given a considerable boost in the knowledge of the lncRNA functions. In recent years, single-cell transcription variability was associated with non-coding RNA expression, revealing this class of RNAs as important transcripts in the cell lineage specification. The purpose of this review is to collect updated information about lncRNA classification and new findings on their function derived from single-cell analysis. We also retained useful for all researchers to describe the methods available for single-cell analysis and the databases collecting single-cell and lncRNA data. Tables are included to schematize, describe, and compare exposed concepts.Int. J. Mol. Sci. 2020, 21, 302 2 of 32 not achieved by transcription factors (TFs) alone because of their low number (approximately 1500 in mammals [4]) compared to genes to regulate (90% of the genome is transcribed in human [5]). A single TF is estimated to regulate only ten thousand genes according to Chromatin immunoprecipitation and DNA sequencing experiments (ChIp-seq) [6]. Moreover, the involvement of non-coding RNAs in gene expression regulation was evident [7,8]. The evidence that the non-protein coding component of the human genome is actively transcribed and carries out crucial functions limits the importance of coding genes in genome regulation. Non-coding transcripts become one of the stars of modern biology, especially because of their involvement in a wide range of regulatory processes and changed the association of non-coding regions to junk DNA [3].The genome of multicellular eukaryotes is mostly comprised of non-coding DNA (less than 2% of the human genome codes for proteins [9] while 90% of the human genome is transcribed). Non-coding DNA is transcribed into different classes of non-coding RNAs (ncRNAs) that include structural RNAs (rRNAs and tRNAs) and regulatory RNAs [10]. rRNAs and tRNAs are involved in mRNA translation and can be long or short in size. Regulatory RNAs are further divided into three classes: Small, medium, and long non-coding RNAs. Small non-coding RNAs are between 20 and 50 nucleotides long and include microRNAs (miRNAs) that participate in post-transcriptional regulation [11], small interfering RNAs (siRNAs) that are double-stranded RNAs involved in gene silencing through RNA interfering pathway [12], piwi interacting R...

show abstract

Section: Discussionmentioning

confidence: 99%

A Single Cell but Many Different Transcripts: A Journey into the World of Long Non-Coding RNAs

Alessio

Bonadio

Buson

et al. 2020

IJMS

View full text Add to dashboard Cite

show abstract

“…As major tissue, organ, and organism expression atlases are increasingly generated (44,45), it is critical to also decipher mechanistic gene regulatory programs for refining functional cell state and cell type definitions. Here, we highlight a computational approach to infer regulons and link their specific cell type activity with functional roles, from integrated scRNA-seq cell atlases.…”

Section: Discussionmentioning

confidence: 99%

Predicting gene regulatory networks from cell atlases

Møller

Natarajan

2020

Life Sci. Alliance

View full text Add to dashboard Cite

Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type–specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.

show abstract

“…Indeed, the next years will probably see an even bigger explosion of single cell papers characterizing PDAC, as the Human Cell Atlas (HCA) project [70] and Human Biomolecular Atlas Program (HuBMAP) [71] were already launched, with the aim of dissect every single cell of our body (in health and in disease) using single cell genomic tools. The first papers from HCA were already published [72,73], and they greatly expanded our previous knowledge of maternal-fetal interface [72] and thymus [73], with novel cellular subpopulation described for the first time.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Pancreatic Cancer Molecular Classifications: From Bulk Genomics to Single Cell Analysis

Pompella

Tirino

Pappalardo

et al. 2020

IJMS

View full text Add to dashboard Cite

Pancreatic cancer represents one of the most lethal disease worldwide but still orphan of a molecularly driven therapeutic approach, although many genomic and transcriptomic classifications have been proposed over the years. Clinical heterogeneity is a hallmark of this disease, as different patients show different responses to the same therapeutic regimens. However, genomic analyses revealed quite a homogeneous disease picture, with very common mutations in four genes only (KRAS, TP53, CDKN2A, and SMAD4) and a long tail of other mutated genes, with doubtful pathogenic meaning. Even bulk transcriptomic classifications could not resolve this great heterogeneity, as many informations related to small cell populations within cancer tissue could be lost. At the same time, single cell analysis has emerged as a powerful tool to dissect intratumoral heterogeneity like never before, with possibility of generating a new disease taxonomy at unprecedented molecular resolution. In this review, we summarize the most relevant genomic, bulk and single-cell transcriptomic classifications of pancreatic cancer, and try to understand how novel technologies, like single cell analysis, could lead to novel therapeutic strategies for this highly lethal disease.

show abstract

The human body at cellular resolution: the NIH Human Biomolecular Atlas Program

Cited by 469 publications

References 46 publications

A Single Cell but Many Different Transcripts: A Journey into the World of Long Non-Coding RNAs

A Single Cell but Many Different Transcripts: A Journey into the World of Long Non-Coding RNAs

Predicting gene regulatory networks from cell atlases

Pancreatic Cancer Molecular Classifications: From Bulk Genomics to Single Cell Analysis

Contact Info

Product

Resources

About