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Creators of software widely used in computational biology discuss the factors that contributed to their success

Monocle 2 uses reversed graph embedding to automatically learn complex, branched pseudotime trajectories of differentiation or cellular state changes from single-cell expression data.

Monocle 2 uses reversed graph embedding to automatically learn complex, branched pseudotime trajectories of differentiation or cellular state changes from single-cell expression data.

The Census tool converts single-cell RNA-seq relative read counts to relative transcript counts for more accurate differential gene expression and analysis in the absence of spike-ins or molecular barcodes.

Transcriptional profiling of 61,052 cells undergoing epithelial-to-mesenchymal transition (EMT) shows continuous waves of gene regulation, not discrete stages. A CRISPR screen orders EMT regulators along EMT progression.

Garnett uses a hierarchical markup language and machine learning to define cell types and their marker genes and identifies these cell types in scRNA-seq datasets from tissues and whole organisms and across species.

Dimensionality reduction is often used to visualize expression profiling data in order to find relationships among cells. Here, the authors use Uniform Manifold Approximation and Projection (UMAP) on published expression data of gene deletions of S. cerevisiae to find novel protein interactions.

We present the ‘zebrafish single-cell atlas of perturbed embryos’, single-cell trancriptomic data of developing zebrafish embryos across various timepoints and with genetic perturbations.

Data from single-cell combinatorial-indexing RNA-sequencing analysis of 2 million cells from mouse embryos between embryonic days 9.5 and 13.5 are compiled in a cell atlas of mouse organogenesis, which provides a global view of developmental processes occurring during this critical period.

This study reports the correction of pulmonary alveolar proteinosis (PAP) in Csf2rb^–/– mice by a single transfer of either wild-type or gene-corrected macrophages directly to the lungs — the transplanted macrophages persisted for at least 1 year; this transplantation strategy obviated the need for myeloablation and immunosuppression and should be a feasible therapy for humans with hereditary PAP.

Integrative analysis of single-cell RNA-sequencing datasets across mouse gastrulation and organogenesis identifies cell states and trajectories at successive developmental stages, along with transcription factors that could potentially mediate lineage choices.

Single-cell, spatial and multi-omic profiling technologies generate large-scale data that reveal the output of genome-scale experiments across diverse cells, tissues and organisms. Cole Trapnell reviews the underlying core statistical challenges that need to be tackled to harness the power of these technologies and advance our understanding of gene function in health and disease.

Long-read chromatin assay reveals (1) a novel class of accessible chromatin regions, (2) accessibility within individual LTR retrotransposons and (3) the relationship between diffuse accessibility, gene body methylation and hAT transposon insertion.

Using spike-in controls with current single cell RNA-seq platforms remains a challenge. Here, the authors use a mixture of short, unmodified DNA oligos as a normalization standard for sci-RNAseq to improve the detection of global transcriptome changes.

Itkin et al. identify a role for Fli-1 in hematopoietic stem cell activation during regenerative hematopoiesis. They show that Fli-1 coordinates hematopoietic stem cells to stimulate niche-derived Notch1 feedback signals for demand-needed hematopoietic cell output.

Here, the authors use single cell RNA-sequencing to generate an atlas of signaling interactions regulating embryonic hematopoietic stem cell (HSC) development and apply this knowledge to engineer a niche sufficient to support HSC maturation in vitro.

Mapping the vast quantities of short sequence fragments produced by next-generation sequencing platforms is a challenge. What programs are available and how do they work?

RNA-Seq enables rapid sequencing of total cellular RNA and should allow the reconstruction of spliced transcripts in a cell population. Trapnell et al. achieve this and transcript quantification using only paired-end RNA-Seq data and an unannotated genome sequence, and apply the approach to characterize isoform switching over a developmental time course.

The Cuffdiff 2 algorithm improves analysis of RNA-Seq data by accounting for sample-to-sample biological variability and the complexity of transcript isoforms.

A study reports single-cell RNA-sequencing profiles for more than 1.6 million cell nuclei from 101 whole mouse embryos including 22 mutant and 4 wild-type genotypes, from one experiment.

Single-cell quantitative expression reporters enable high-sensitivity quantitative characterization of cis-regulatory elements at the single-cell level in multicellular systems.

Single-cell transcriptome profiling of mouse embryos and newborn pups is combined with previously published data to construct a tree of cell-type relationships tracing development from zygote to birth.

CRISPR-based single-cell pooled screens that use linked barcodes suffer from lost sensitivity due to lentiviral template switching. The barcode-free CROP-seq design circumvents this problem.

Gene expression dynamics in single cells are tracked by labeling newly synthesized RNA in indexed cells.

A long intergenic noncoding RNA, Firre, is now shown to localize to a domain across its own chromosomal locus and to distinct interacting transchromosomal loci in mouse and human cells. In addition, Firre interacts with nuclear-matrix factor hnRNPU. These results lead to a model in which Firre functions as a nuclear-organization factor modulating the topological organization of multiple chromosomes.

An algorithm uncovers transcriptome dynamics during differentiation by ordering RNA-Seq data from single cells.

Analysis of single-nucleus RNA- and ATAC-Seq from healthy human heart revealed age- and sex-specific alterations, defines candidate developmental stage-specific regulatory patterns, and identifies cell type-specific regulatory signatures.

Rare transcripts remain enigmatic in part because they are difficult to detect robustly on a large scale. Mercer et al. show that targeted RNA sequencing after array capture can reach saturating depth at the targeted loci and reveal unprecedented levels of rare noncoding transcripts and previously unrecognized spliced variants from important loci such as p53 and HOX.

Methylation profiling of single cells in high throughput achieves read-alignment rates approaching bulk cell population studies.

An improved assay for chromatin accessibility at single-cell resolution in Drosophila melanogaster embryos enables identification of developmental-stage- and cell-lineage-specific patterns of chromatin-level transcriptional regulation.

This protocol presents an optimized version of the single-cell combinatorial indexing RNA sequencing protocol that is faster, less expensive and suitable for profiling tissues rich in RNases, as well as a ‘Tiny-Sci’ protocol for limited input samples.

HuBMAP supports technology development, data acquisition, and spatial analyses to generate comprehensive molecular and cellular three-dimensional tissue maps.

The Human BioMolecular Atlas Program (HuBMAP) presents its production phase: the generation of spatial maps of functional tissue units across organs from diverse populations and the creation of tools and infrastructure to advance biomedical research.