Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

Modular derivation of diverse, regionally discrete human posterior CNS neurons enables discovery of transcriptomic patterns.

Our inability to derive the neuronal diversity that comprises the posterior central nervous system (pCNS) using human pluripotent stem cells (hPSCs) poses an impediment to understanding human neurodevelopment and disease in the hindbrain and spinal cord. Here, we establish a modular, monolayer differentiation paradigm that recapitulates both rostrocaudal (R/C) and dorsoventral (D/V) patterning, enabling derivation of diverse pCNS neurons with discrete regional specificity. First, neuromesodermal progenitors (NMPs) with discrete HOX profiles are converted to pCNS progenitors (pCNSPs). Then, by tuning D/V signaling, pCNSPs are directed to locomotor or somatosensory neurons. Expansive single-cell RNA-sequencing (scRNA-seq) analysis coupled with a novel computational pipeline allowed us to detect hundreds of transcriptional markers within region-specific phenotypes, enabling discovery of gene expression patterns across R/C and D/V developmental axes. These findings highlight the potential of these resources to advance a mechanistic understanding of pCNS development, enhance in vitro models, and inform therapeutic strategies.

Defining Reprogramming Checkpoints from Single-Cell Analyses of Induced Pluripotency.

Elucidating the mechanism of reprogramming is confounded by heterogeneity due to the low efficiency and differential kinetics of obtaining induced pluripotent stem cells (iPSCs) from somatic cells. Therefore, we increased the efficiency with a combination of epigenomic modifiers and signaling molecules and profiled the transcriptomes of individual reprogramming cells. Contrary to the established temporal order, somatic gene inactivation and upregulation of cell cycle, epithelial, and early pluripotency genes can be triggered independently such that any combination of these events can occur in single cells. Sustained co-expression of Epcam, Nanog, and Sox2 with other genes is required to progress toward iPSCs. Ehf, Phlda2, and translation initiation factor Eif4a1 play functional roles in robust iPSC generation. Using regulatory network analysis, we identify a critical role for signaling inhibition by 2i in repressing somatic expression and synergy between the epigenomic modifiers ascorbic acid and a Dot1L inhibitor for pluripotency gene activation.

The complete mitochondrial genome of Hine's emerald dragonfly (Somatochlora hineana Williamson) via NGS sequencing.

Here, we report the complete mitochondrial genome of the endangered Hine's emerald dragonfly (HED), Somatochlora hineana Williamson. Data were generated via next generation sequencing (NGS) and assembled using a mitochondrial baiting and iterative mapping approach. The full length circular genome is 15,705 bp with 26.6% GC content. It contains the typical metazoan set of 37 genes: 13 protein-coding genes, 22 transfer RNA (tRNA) and 2 ribosomal RNA (rRNA) genes, and an A + T-rich control region. To our knowledge, this is the first report of the complete HED mitogenome.