Single cell transcriptomics of pluripotent stem cells: reprogramming and differentiation

A Deep-Learning Proteomic-Scale Approach for Drug Design

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded "objective" signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

Reprogramming lineage identity through cell-cell fusion

The conversion of differentiated cells to a pluripotent state through somatic cell nuclear transfer provided the first unequivocal evidence that differentiation was reversible. In more recent times, introducing a combination of key transcription factors into terminally differentiated mammalian cells was shown to drive their conversion to induced pluripotent stem cells (iPSCs). These discoveries were transformative, but the relatively slow speed (2-3 weeks) and low efficiency of reprogramming (0.1-1%) made deciphering the underlying molecular mechanisms difficult and complex. Cell fusion provides an alternative reprogramming approach that is both efficient and tractable, particularly when combined with modern multi-omics analysis of individual cells. Here we review the history and the recent advances in cell-cell fusion that are enabling a better understanding cell fate conversion, and we discuss how this knowledge could be used to shape improved strategies for regenerative medicine.

Transcriptional dynamics of induced pluripotent stem cell differentiation into β cells reveals full endodermal commitment and homology with human islets

BACKGROUND AIMS

Induced pluripotent stem cells (iPSCs) have the capacity to generate β cells in vitro, but the differentiation is incomplete and generates a variable percentage of off-target cells. Single-cell RNA sequencing offers the possibility of characterizing the transcriptional dynamics throughout differentiation and determining the identity of the final differentiation product.

METHODS

Single-cell transcriptomics data were obtained from four stages across differentiation of iPSCs into β cells and from human donor islets.

RESULTS

Clustering analysis revealed that iPSCs undertake a full endoderm commitment, and the obtained endocrine pancreatic cells have high homology with mature islets. The iPSC-derived β cells were devoid of pluripotent residual cells, and the differentiation was pancreas-specific, as it did not generate ectodermal or mesodermal cells. Pseudotime trajectory identified a dichotomic endocrine/non-endocrine cell fate and distinct subgroups in the endocrine branch.

CONCLUSIONS

Future efforts to produce β cells from iPSCs must aim not only to improve the resulting endocrine cell but also to avoid differentiation into non-pancreatic endoderm cells.

Predicting gene regulatory networks from cell atlases

Integrated single-cell gene regulatory network from three mouse cell atlases captures global and cell type–specific regulatory modules and crosstalk, important for cellular identity.

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.

Direct Lineage Reprogramming: Harnessing Cell Plasticity between Liver and Pancreas

Direct lineage reprogramming of abundant and accessible cells into therapeutically useful cell types holds tremendous potential in regenerative medicine. To date, a number of different cell types have been generated by lineage reprogramming methods, including cells from the neural, cardiac, hepatic, and pancreatic lineages. The success of this strategy relies on developmental biology and the knowledge of cell-fate-defining transcriptional networks. Hepatocytes represent a prime target for β cell conversion for numerous reasons, including close developmental origin, accessibility, and regenerative potential. We present here an overview of pancreatic and hepatic development, with a particular focus on the mechanisms underlying the divergence between the two cell lineages. Additionally, we discuss to what extent this lineage relationship can be exploited in efforts to reprogram one cell type into the other and whether such an approach may provide a suitable strategy for regenerative therapies of diabetes.

Transcriptome profiles of isolated murine Achilles tendon proper- and peritenon-derived progenitor cells

Progenitor cells of the tendon proper and peritenon have unique properties that could impact their utilization in tendon repair strategies. While a few markers have been found to aid in distinguishing progenitors cells from each region, there is great value in identifying more markers. In this study, we hypothesized that RNAseq could be used to improve our understanding of those markers that define these cell types. Transcriptome profiles were generated for pools of mouse Achilles tendon progenitor cells from both regions and catalogues of potential markers were generated. Moreover, common (e.g., glycoprotein, signaling, and proteinaceous extracellular matrix) and unique (e.g., cartilage development versus angiogenesis and muscle contraction) biological processes and molecular functions were described for progenitors from each region. Real-time quantitative PCR of a subset of genes was used to gain insight into the heterogeneity amongst individual progenitor colonies from each region. Markers like Scx, Mkx, Thbs4, and Wnt10a were consistently able to distinguish tendon proper progenitors from peritenon progenitors; expression variability for other genes suggested greater cell type complexity for potential peritenon progenitor markers. This is the first effort to define Achilles tendon progenitor markers by region. Further efforts to investigate the value of these cataloged markers are required by screening more individual colonies of progenitors for more markers. Clinical Significance: Findings from this study advance efforts in the discernment of cell type specific markers for tendon proper and peritenon progenitor cells; insight into marker sets could improve tracking and sorting strategies for these cells for future therapeutic strategies. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1409-1418, 2019.

Comparative analysis of sequencing technologies for single-cell transcriptomics

Single-cell RNA-seq technologies require library preparation prior to sequencing. Here, we present the first report to compare the cheaper BGISEQ-500 platform to the Illumina HiSeq platform for scRNA-seq. We generate a resource of 468 single cells and 1297 matched single cDNA samples, performing SMARTer and Smart-seq2 protocols on two cell lines with RNA spike-ins. We sequence these libraries on both platforms using single- and paired-end reads. The platforms have comparable sensitivity and accuracy in terms of quantification of gene expression, and low technical variability. Our study provides a standardized scRNA-seq resource to benchmark new scRNA-seq library preparation protocols and sequencing platforms.

Single-Cell Tagged Reverse Transcription (STRT-Seq)

Single-cell RNA sequencing (scRNA-seq) has become an established approach to profile entire transcriptomes of individual cells from different cell types, tissues, species, and organisms. Single-cell tagged reverse transcription sequencing (STRT-seq) is one of the early single-cell methods which utilize 5' tag counting of transcripts. STRT-seq performed on microfluidics Fluidigm C1 platform (STRT-C1) is a flexible scRNA-seq approach that allows for accurate, sensitive and importantly molecular counting of transcripts at single-cell level. Herein, I describe the STRT-C1 method and the steps involved in capturing 96 cells across C1 microfluidics chip, cDNA synthesis, and preparing single-cell libraries for Illumina short-read sequencing.

Endothelial stem and progenitor cells (stem cells): (2017 Grover Conference Series)

The capacity of existing blood vessels to give rise to new blood vessels via endothelial cell sprouting is called angiogenesis and is a well-studied biologic process. In contrast, little is known about the mechanisms for endothelial cell replacement or regeneration within established blood vessels. Since clear definitions exist for identifying cells with stem and progenitor cell properties in many tissues and organs of the body, several groups have begun to accumulate evidence that endothelial stem and progenitor cells exist within the endothelial intima of existing blood vessels. This paper will review stem and progenitor cell definitions and highlight several recent papers purporting to have identified resident vascular endothelial stem and progenitor cells.