Platform-agnostic CellNet enables cross-study analysis of cell fate engineering protocols.

Optimization of cell engineering protocols requires standard, comprehensive quality metrics. We previously developed CellNet, a computational tool to quantitatively assess the transcriptional fidelity of engineered cells compared with their natural counterparts, based on bulk-derived expression profiles. However, this platform and others were limited in their ability to compare data from different sources, and no current tool makes it easy to compare new protocols with existing state-of-the-art protocols in a standardized manner. Here, we utilized our prior application of the top-scoring pair transformation to build a computational platform, platform-agnostic CellNet (PACNet), to address both shortcomings. To demonstrate the utility of PACNet, we applied it to thousands of samples from over 100 studies that describe dozens of protocols designed to produce seven distinct cell types. We performed an in-depth examination of hepatocyte and cardiomyocyte protocols to identify the best-performing methods, characterize the extent of intra-protocol and inter-lab variation, and identify common off-target signatures, including a surprising neural/neuroendocrine signature in primary liver-derived organoids. We have made PACNet available as an easy-to-use web application, allowing users to assess their protocols relative to our database of reference engineered samples, and as open-source, extensible code.

Comprehensive DNA Methylation Analysis Indicates That Pancreatic Intraepithelial Neoplasia Lesions Are Acinar-Derived and Epigenetically Primed for Carcinogenesis.

Pancreatic ductal adenocarcinoma (PDAC) is believed to arise from the accumulation of a series of somatic mutations and is also frequently associated with pancreatic intraepithelial neoplasia (PanIN) lesions. However, there is still debate as to whether the cell type-of-origin of PanINs and PDACs in humans is acinar or ductal. As cell type identity is maintained epigenetically, DNA methylation changes during pancreatic neoplasia can provide a compelling perspective to examine this question. Here, we performed laser-capture microdissection on surgically resected specimens from 18 patients to isolate, with high purity, DNA for whole-genome bisulfite sequencing from four relevant cell types: acini, nonneoplastic ducts, PanIN lesions, and PDAC lesions. Differentially methylated regions (DMR) were identified using two complementary analytical approaches: bsseq, which identifies any DMRs but is particularly useful for large block-like DMRs, and informME, which profiles the potential energy landscape across the genome and is particularly useful for identifying differential methylation entropy. Both global methylation profiles and block DMRs clearly implicated an acinar origin for PanINs. At the gene level, PanIN lesions exhibited an intermediate acinar-ductal phenotype resembling acinar-to-ductal metaplasia. In 97.6% of PanIN-specific DMRs, PanIN lesions had an intermediate methylation level between normal and PDAC, which suggests from an information theory perspective that PanIN lesions are epigenetically primed to progress to PDAC. Thus, epigenomic analysis complements histopathology to define molecular progression toward PDAC. The shared epigenetic lineage between PanIN and PDAC lesions could provide an opportunity for prevention by targeting aberrantly methylated progression-related genes. SIGNIFICANCE: Analysis of DNA methylation landscapes provides insights into the cell-of-origin of PanIN lesions, clarifies the role of PanIN lesions as metaplastic precursors to human PDAC, and suggests potential targets for chemoprevention.

Effective range of non-cell autonomous activator and inhibitor peptides specifying plant stomatal patterning.

Stomata are epidermal valves that facilitate gas exchange between plants and their environment. Stomatal patterning is regulated by the EPIDERMAL PATTERING FACTOR (EPF) family of secreted peptides: EPF1 enforces stomatal spacing, whereas EPIDERMAL PATTERNING FACTOR-LIKE9 (EPFL9), also known as Stomagen, promotes stomatal development. It remains unknown, however, how far these signaling peptides act. Utilizing Cre-lox recombination-based mosaic sectors that overexpress either EPF1 or Stomagen in Arabidopsis cotyledons, we reveal a range within the epidermis and across the cell layers in which these peptides influence patterns. To determine their effective ranges quantitatively, we developed a computational pipeline, SPACE (stomata patterning autocorrelation on epidermis), that describes probabilistic two-dimensional stomatal distributions based upon spatial autocorrelation statistics used in astrophysics. The SPACE analysis shows that, whereas both peptides act locally, the inhibitor EPF1 exerts longer range effects than the activator Stomagen. Furthermore, local perturbation of stomatal development has little influence on global two-dimensional stomatal patterning. Our findings conclusively demonstrate the nature and extent of EPF peptides as non-cell autonomous local signals and provide a means for quantitative characterization of complex spatial patterns in development.This article has an associated 'The people behind the papers' interview.

Evaluation of Bunina et al.: Synthesizing Multi-omics Data to Delineate Neuronal Differentiation.

One snapshot of the peer review process for "Genomic rewiring of SOX2 chromatin interaction network during differentiation of ESCs to postmitotic neurons" (Bunina et al., 2020).

Cell Fate Engineering Tools for iPSC Disease Modeling.

The field of cell fate engineering is contingent on tools that can quantitatively assess the efficacy of cell fate engineering protocols and experiments. CellNet is such a cell fate assessment tool that utilizes network biology to both evaluate and suggest candidate transcriptional regulatory modifications to improve the similarity of an engineered population to its corresponding in vivo target population. CellNet takes in expression profiles in the form of RNA-sequencing data and generates several metrics of cell identity and protocol efficacy. In this chapter, we demonstrate how to (1) preprocess raw RNA-sequencing data to generate an expression matrix, (2) train CellNet using preprocessed expression matrices, and (3) apply CellNet to a query study and interpret its results. We demonstrate the utility of CellNet for analysis of iPSC disease modeling studies, which we evaluate through the lens of cell fate engineering.

Assessment of engineered cells using CellNet and RNA-seq.

CellNet is a computational platform designed to assess cell populations engineered by either directed differentiation of pluripotent stem cells (PSCs) or direct conversion, and to suggest specific hypotheses to improve cell fate engineering protocols. CellNet takes as input gene expression data and compares them with large data sets of normal expression profiles compiled from public sources, in regard to the extent to which cell- and tissue-specific gene regulatory networks are established. CellNet was originally designed to work with human or mouse microarray expression data for 21 cell or tissue (C/T) types. Here we describe how to apply CellNet to RNA-seq data and how to build a completely new CellNet platform applicable to, for example, other species or additional cell and tissue types. Once the raw data have been preprocessed, running CellNet takes only several minutes, whereas the time required to create a completely new CellNet is several hours.