The impact of AI on research.

Large and complex datasets have made artificial intelligence (AI) an invaluable tool for discovery across biological research. We asked experts how AI has impacted their work. Their experiences and perspectives offer thoughtful insights into potential offered by AI for their fields.

Self-Reporting Transposons Enable Simultaneous Readout of Gene Expression and Transcription Factor Binding in Single Cells.

Cellular heterogeneity confounds in situ assays of transcription factor (TF) binding. Single-cell RNA sequencing (scRNA-seq) deconvolves cell types from gene expression, but no technology links cell identity to TF binding sites (TFBS) in those cell types. We present self-reporting transposons (SRTs) and use them in single-cell calling cards (scCC), a novel assay for simultaneously measuring gene expression and mapping TFBS in single cells. The genomic locations of SRTs are recovered from mRNA, and SRTs deposited by exogenous, TF-transposase fusions can be used to map TFBS. We then present scCC, which map SRTs from scRNA-seq libraries, simultaneously identifying cell types and TFBS in those same cells. We benchmark multiple TFs with this technique. Next, we use scCC to discover BRD4-mediated cell-state transitions in K562 cells. Finally, we map BRD4 binding sites in the mouse cortex at single-cell resolution, establishing a new method for studying TF biology in situ.

CellNet: network biology applied to stem cell engineering.

Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering.

Dissecting engineered cell types and enhancing cell fate conversion via CellNet.

Engineering clinically relevant cells in vitro holds promise for regenerative medicine, but most protocols fail to faithfully recapitulate target cell properties. To address this, we developed CellNet, a network biology platform that determines whether engineered cells are equivalent to their target tissues, diagnoses aberrant gene regulatory networks, and prioritizes candidate transcriptional regulators to enhance engineered conversions. Using CellNet, we improved B cell to macrophage conversion, transcriptionally and functionally, by knocking down predicted B cell regulators. Analyzing conversion of fibroblasts to induced hepatocytes (iHeps), CellNet revealed an unexpected intestinal program regulated by the master regulator Cdx2. We observed long-term functional engraftment of mouse colon by iHeps, thereby establishing their broader potential as endoderm progenitors and demonstrating direct conversion of fibroblasts into intestinal epithelium. Our studies illustrate how CellNet can be employed to improve direct conversion and to uncover unappreciated properties of engineered cells.

Dissecting cell identity via network inference and in silico gene perturbation.

Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms-mouse and human haematopoiesis, and zebrafish embryogenesis-and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto, an established notochord regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a. Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation.

Stem cell dynamics and cellular heterogeneity across lineage subtypes of castrate-resistant prostate cancer.

To resist lineage-dependent therapies such as androgen receptor inhibition, prostate luminal epithelial adenocarcinoma cells often adopt a stem-like state resulting in lineage plasticity and phenotypic heterogeneity. Castrate-resistant prostate adenocarcinoma can transition to neuroendocrine (NE) and occasionally to amphicrine, co-expressed luminal and NE, phenotypes. We developed castrate-resistant prostate cancer (CRPC) patient-derived organoid models that preserve heterogeneity of the originating tumor, including an amphicrine model displaying a range of luminal and NE phenotypes. To gain biological insight and to identify potential treatment targets within heterogeneous tumor cell populations, we assessed the lineage hierarchy and molecular characteristics of various CRPC tumor subpopulations. Transcriptionally similar stem/progenitor (St/Pr) cells were identified for all lineage populations. Lineage tracing in amphicrine CRPC showed that heterogeneity originated from distinct subclones of infrequent St/Pr cells that produced mainly quiescent differentiated amphicrine progeny. By contrast, adenocarcinoma CRPC progeny originated from St/Pr cells and self-renewing differentiated luminal cells. Neuroendocrine prostate cancer (NEPC) was composed almost exclusively of self-renewing St/Pr cells. Amphicrine subpopulations were enriched for secretory luminal, mesenchymal, and enzalutamide treatment persistent signatures that characterize clinical progression. Finally, the amphicrine St/Pr subpopulation was specifically depleted with an AURKA inhibitor, which blocked tumor growth. These data illuminate distinct stem cell (SC) characteristics for subtype-specific CRPC in addition to demonstrating a context for targeting differentiation-competent prostate SCs.

Single-cell mapping of lineage and identity in direct reprogramming.

Direct lineage reprogramming involves the conversion of cellular identity. Single-cell technologies are useful for deconstructing the considerable heterogeneity that emerges during lineage conversion. However, lineage relationships are typically lost during cell processing, complicating trajectory reconstruction. Here we present 'CellTagging', a combinatorial cell-indexing methodology that enables parallel capture of clonal history and cell identity, in which sequential rounds of cell labelling enable the construction of multi-level lineage trees. CellTagging and longitudinal tracking of fibroblast to induced endoderm progenitor reprogramming reveals two distinct trajectories: one leading to successfully reprogrammed cells, and one leading to a 'dead-end' state, paths determined in the earliest stages of lineage conversion. We find that expression of a putative methyltransferase, Mettl7a1, is associated with the successful reprogramming trajectory; adding Mettl7a1 to the reprogramming cocktail increases the yield of induced endoderm progenitors. Together, these results demonstrate the utility of our lineage-tracing method for revealing the dynamics of direct reprogramming.

The evolving concept of cell identity in the single cell era.

Fueled by recent advances in single cell biology, we are moving away from qualitative and undersampled assessments of cell identity, toward building quantitative, high-resolution cell atlases. However, it remains challenging to precisely define cell identity, leading to renewed debate surrounding this concept. Here, I present three pillars that I propose are central to the notion of cell identity: phenotype, lineage and state. I explore emerging technologies that are enabling the systematic and unbiased quantification of these properties, and outline how these efforts will enable the construction of a high-resolution, dynamic landscape of cell identity, potentially revealing its underlying molecular regulation to provide new opportunities for understanding and manipulating cell fate.

Direct lineage reprogramming via pioneer factors; a detour through developmental gene regulatory networks.

Although many approaches have been employed to generate defined fate in vitro, the resultant cells often appear developmentally immature or incompletely specified, limiting their utility. Growing evidence suggests that current methods of direct lineage conversion may rely on the transition through a developmental intermediate. Here, I hypothesize that complete conversion between cell fates is more probable and feasible via reversion to a developmentally immature state. I posit that this is due to the role of pioneer transcription factors in engaging silent, unmarked chromatin and activating hierarchical gene regulatory networks responsible for embryonic patterning. Understanding these developmental contexts will be essential for the precise engineering of cell identity.

Making a firm decision: multifaceted regulation of cell fate in the early mouse embryo.

The preimplantation mammalian embryo offers a striking opportunity to address the question of how and why apparently identical cells take on separate fates. Two cell fate decisions are taken before the embryo implants; these decisions set apart a group of pluripotent cells, progenitors for the future body, from the distinct extraembryonic lineages of trophectoderm and primitive endoderm. New molecular, cellular and developmental insights reveal the interplay of transcriptional regulation, epigenetic modifications, cell position and cell polarity in these two fate decisions in the mouse. We discuss how mechanisms proposed in previously distinct models might work in concert to progressively reinforce cell fate decisions through feedback loops.

Zcchc11 uridylates mature miRNAs to enhance neonatal IGF-1 expression, growth, and survival.

The Zcchc11 enzyme is implicated in microRNA (miRNA) regulation. It can uridylate let-7 precursors to decrease quantities of the mature miRNA in embryonic stem cell lines, suggested to mediate stem cell maintenance. It can uridylate mature miR-26 to relieve silencing activity without impacting miRNA content in cancer cell lines, suggested to mediate cytokine and growth factor expression. Broader roles of Zcchc11 in shaping or remodeling the miRNome or in directing biological or physiological processes remain entirely speculative. We generated Zcchc11-deficient mice to address these knowledge gaps. Zcchc11 deficiency had no impact on embryogenesis or fetal development, but it significantly decreased survival and growth immediately following birth, indicating a role for this enzyme in early postnatal fitness. Deep sequencing of small RNAs from neonatal livers revealed roles of this enzyme in miRNA sequence diversity. Zcchc11 deficiency diminished the lengths and terminal uridine frequencies for diverse mature miRNAs, but it had no influence on the quantities of any miRNAs. The expression of IGF-1, a liver-derived protein essential to early growth and survival, was enhanced by Zcchc11 expression in vitro, and miRNA silencing of IGF-1 was alleviated by uridylation events observed to be Zcchc11-dependent in the neonatal liver. In neonatal mice, Zcchc11 deficiency significantly decreased IGF-1 mRNA in the liver and IGF-1 protein in the blood. We conclude that the Zcchc11-mediated terminal uridylation of mature miRNAs is pervasive and physiologically significant, especially important in the neonatal period for fostering IGF-1 expression and enhancing postnatal growth and survival. We propose that the miRNA 3' terminus is a regulatory node upon which multiple enzymes converge to direct silencing activity and tune gene expression.

Origin and formation of the first two distinct cell types of the inner cell mass in the mouse embryo.

A crucial question in mammalian development is how cells of the early embryo differentiate into distinct cell types. The first decision is taken when cells undertake waves of asymmetric division that generate one daughter on the inside and one on the outside of the embryo. After this division, some cells on the inside remain pluripotent and give rise to the epiblast, and hence the future body, whereas others develop into the primitive endoderm, an extraembryonic tissue. How the fate of these inside cells is decided is unknown: Is the process random, or is it related to their developmental origins? To address this question, we traced all cells by live-cell imaging in intact, unmanipulated embryos until the epiblast and primitive endoderm became distinct. This analysis revealed that inner cell mass (ICM) cells have unrestricted developmental potential. However, cells internalized by the first wave of asymmetric divisions are biased toward forming pluripotent epiblast, whereas cells internalized in the next two waves of divisions are strongly biased toward forming primitive endoderm. Moreover, we show that cells internalized by the second wave up-regulate expression of Gata6 and Sox17, and changing the expression of these genes determines whether the cells become primitive endoderm. Finally, with our ability to determine the origin of cells, we find that inside cells that are mispositioned when they are born can sort into the correct layer. In conclusion, we propose a model in which the timing of cell internalization, cell position, and cell sorting combine to determine distinct lineages of the preimplantation mouse embryo.

Single-cell lineage tracing reveals hierarchy and mechanism of adipocyte precursor maturation.

White adipose tissue is crucial in various physiological processes. In response to high caloric intake, adipose tissue may expand by generating new adipocytes. Adipocyte precursor cells (progenitors and preadipocytes) are essential for generating mature adipocytes, and single-cell RNA sequencing provides new means to identify these populations. Here, we characterized adipocyte precursor populations in the skin, an adipose depot with rapid and robust generation of mature adipocytes. We identified a new population of immature preadipocytes, revealed a biased differentiation potential of progenitor cells, and identified Sox9 as a critical factor in driving progenitors toward adipose commitment, the first known mechanism of progenitor differentiation. These findings shed light on the specific dynamics and molecular mechanisms underlying rapid adipogenesis in the skin.

Gene regulatory network reconfiguration in direct lineage reprogramming.

In direct lineage conversion, transcription factor (TF) overexpression reconfigures gene regulatory networks (GRNs) to reprogram cell identity. We previously developed CellOracle, a computational method to infer GRNs from single-cell transcriptome and epigenome data. Using inferred GRNs, CellOracle simulates gene expression changes in response to TF perturbation, enabling in silico interrogation of network reconfiguration. Here, we combine CellOracle analysis with lineage tracing of fibroblast to induced endoderm progenitor (iEP) conversion, a prototypical direct reprogramming paradigm. By linking early network state to reprogramming outcome, we reveal distinct network configurations underlying successful and failed fate conversion. Via in silico simulation of TF perturbation, we identify new factors to coax cells into successfully converting their identity, uncovering a central role for the AP-1 subunit Fos with the Hippo signaling effector, Yap1. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations, providing new mechanistic insights into lineage reprogramming.

Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes.

Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage-tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics. However, reliance on transcriptional profiling limits adaptation to other single-cell assays. With CellTag-multi, we present an approach that enables direct capture of heritable random barcodes expressed as polyadenylated transcripts, in both single-cell RNA sequencing and single-cell Assay for Transposase Accessible Chromatin using sequencing assays, allowing for independent clonal tracking of transcriptional and epigenomic cell states. We validate CellTag-multi to characterize progenitor cell lineage priming during mouse hematopoiesis. Additionally, in direct reprogramming of fibroblasts to endoderm progenitors, we identify core regulatory programs underlying on-target and off-target fates. Furthermore, we reveal the transcription factor Zfp281 as a regulator of reprogramming outcome, biasing cells toward an off-target mesenchymal fate. Our results establish CellTag-multi as a lineage-tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.

Defining cardiac functional recovery in end-stage heart failure at single-cell resolution.

Recovery of cardiac function is the holy grail of heart failure therapy yet is infrequently observed and remains poorly understood. In this study, we performed single-nucleus RNA sequencing from patients with heart failure who recovered left ventricular systolic function after left ventricular assist device implantation, patients who did not recover and non-diseased donors. We identified cell-specific transcriptional signatures of recovery, most prominently in macrophages and fibroblasts. Within these cell types, inflammatory signatures were negative predictors of recovery, and downregulation of RUNX1 was associated with recovery. In silico perturbation of RUNX1 in macrophages and fibroblasts recapitulated the transcriptional state of recovery. Cardiac recovery mediated by BET inhibition in mice led to decreased macrophage and fibroblast Runx1 expression and diminished chromatin accessibility within a Runx1 intronic peak and acquisition of human recovery signatures. These findings suggest that cardiac recovery is a unique biological state and identify RUNX1 as a possible therapeutic target to facilitate cardiac recovery.