Evolution of myeloproliferative neoplasms from normal blood stem cells.

Over the course of the last decade, genomic studies in the context of normal human hematopoiesis have provided new insights into the early pathogenesis of myeloproliferative neoplasms (MPN). A preclinical phase of MPN, termed clonal hematopoiesis (CH) was identified and subsequent lineage tracing studies revealed a multi-decade long time interval from acquisition of an MPN phenotypic driver mutation in a hematopoietic stem cell (HSC) to the development of overt MPN. Multiple germline variants associated with MPN risk have been identified through genome-wide association studies (GWAS) and in some cases functional interrogation of the impact of the variant has uncovered new insights into HSC biology and MPN development.

scAI-SNP: a method for inferring ancestry from single-cell data.

Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected.

ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets.

Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes.

Deciphering cell states and genealogies of human haematopoiesis.

The human blood system is maintained through the differentiation and massive amplification of a limited number of long-lived haematopoietic stem cells (HSCs). Perturbations to this process underlie diverse diseases, but the clonal contributions to human haematopoiesis and how this changes with age remain incompletely understood. Although recent insights have emerged from barcoding studies in model systems, simultaneous detection of cell states and phylogenies from natural barcodes in humans remains challenging.

An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells.

Motivation: Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms (for instance in gene expression, eclosion, egg-laying, and feeding) tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets, and are also limited by their use of P-values in detecting oscillations.

De novo detection of somatic mutations in high-throughput single-cell profiling data sets.

Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples.

Probe-based bacterial single-cell RNA sequencing predicts toxin regulation.

Clonal bacterial populations rely on transcriptional variation across individual cells to produce specialized states that increase fitness. Understanding all cell states requires studying isogenic bacterial populations at the single-cell level. Here we developed probe-based bacterial sequencing (ProBac-seq), a method that uses libraries of DNA probes and an existing commercial microfluidic platform to conduct bacterial single-cell RNA sequencing.

An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells.

Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms in time series (for instance gene expression, eclosion, egg-laying and feeding) datasets tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets.

Collective polymerase dynamics emerge from DNA supercoiling during transcription.

All biological processes ultimately come from physical interactions. The mechanical properties of DNA play a critical role in transcription. RNA polymerase can over or under twist DNA (referred to as DNA supercoiling) when it moves along a gene, resulting in mechanical stresses in DNA that impact its own motion and that of other polymerases.

Paraxial mesoderm organoids model development of human somites.

During the development of the vertebrate embryo, segmented structures called somites are periodically formed from the presomitic mesoderm (PSM) and give rise to the vertebral column. While somite formation has been studied in several animal models, it is less clear how well this process is conserved in humans. Recent progress has made it possible to study aspects of human paraxial mesoderm (PM) development such as the human segmentation clock using human pluripotent stem cells (hPSCs); however, somite formation has not been observed in these monolayer cultures.

A novel deep learning-based 3D cell segmentation framework for future image-based disease detection.

Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell membrane images. Existing approaches also require fine-tuning multiple manually selected hyperparameters on the new datasets.

Integrating readout of somatic mutations in individual cells with single-cell transcriptional profiling.

In many biological applications, the readout of somatic mutations in individual cells is essential. For example, it can be used to mark individual cancer cells or identify progenies of a stem cell. Here, we present a protocol to perform single-cell RNA-seq and single-cell amplicon-seq using 10X Chromium technology.

Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry.

Most high-dimensional datasets are thought to be inherently low-dimensional-that is, data points are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here, we study the viability of two approaches from differential geometry to estimate the Riemannian curvature of these low-dimensional manifolds. The intrinsic approach relates curvature to the Laplace-Beltrami operator using the heat-trace expansion and is agnostic to how a manifold is embedded in a high-dimensional space.

Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Cancer Cells in Myeloproliferative Neoplasms.

Some cancers originate from a single mutation event in a single cell. Blood cancers known as myeloproliferative neoplasms (MPNs) are thought to originate when a driver mutation is acquired by a hematopoietic stem cell (HSC). However, when the mutation first occurs in individuals and how it affects the behavior of HSCs in their native context is not known.

Evaluation of Arai et al.: What Can Differences across Daughter Cells Tell Us about Stem Cell Renewal?

One snapshot of the peer review process for "Machine learning of hematopoietic stem cell divisions from paired daughter cell expression profiles reveals effects of aging on self-renewal" (Arai et al., 2020).

Metabolic interactions between dynamic bacterial subpopulations.

Individual microbial species are known to occupy distinct metabolic niches within multi-species communities. However, it has remained largely unclear whether metabolic specialization can similarly occur within a clonal bacterial population. More specifically, it is not clear what functions such specialization could provide and how specialization could be coordinated dynamically.

Molecular Time Sharing through Dynamic Pulsing in Single Cells.

In cells, specific regulators often compete for limited amounts of a core enzymatic resource. It is typically assumed that competition leads to partitioning of core enzyme molecules among regulators at constant levels. Alternatively, however, different regulatory species could time share, or take turns utilizing, the core resource.

Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements.

As they proliferate, living cells undergo transitions between specific molecularly and developmentally distinct states. Despite the functional centrality of these transitions in multicellular organisms, it has remained challenging to determine which transitions occur and at what rates without perturbations and cell engineering. Here, we introduce kin correlation analysis (KCA) and show that quantitative cell-state transition dynamics can be inferred, without direct observation, from the clustering of cell states on pedigrees (lineage trees).

Synthetic recording and in situ readout of lineage information in single cells.

Reconstructing the lineage relationships and dynamic event histories of individual cells within their native spatial context is a long-standing challenge in biology. Many biological processes of interest occur in optically opaque or physically inaccessible contexts, necessitating approaches other than direct imaging. Here we describe a synthetic system that enables cells to record lineage information and event histories in the genome in a format that can be subsequently read out of single cells in situ.

Inferring epigenetic dynamics from kin correlations.

Populations of isogenic embryonic stem cells or clonal bacteria often exhibit extensive phenotypic heterogeneity that arises from intrinsic stochastic dynamics of cells. The phenotypic state of a cell can be transmitted epigenetically in cell division, leading to correlations in the states of cells related by descent. The extent of these correlations is determined by the rates of transitions between the phenotypic states.