Search bioRxiv⌕ Search

Biology subjects

Lo, E. K.

Publications and source records attributed to Lo, E. K..

3 recordsLinked to original sources

DNA methylation memory of pancreatic acinar-ductal metaplasia transition state altering Kras-downstream PI3K and Rho GTPase signaling in the absence of Kras mutation

A critical area of recent cancer research is the emergence of transition states between normal and cancer that exhibit increased cell plasticity which underlies tumor cell heterogeneity. Pancreatic ductal adenocarcinoma (PDAC) can arise from the combination of a transition state termed acinar-to-ductal metaplasia (ADM) and a gain-of-function mutation in the proto-oncogene KRAS. During ADM, digestive enzyme-producing acinar cells acquire a transient ductal epithelium-like phenotype while maintaining their geographical acinar organization. One route of ADM initiation is the overexpression of the Kruppel-like factor 4 gene (KLF4) in the absence of oncogenic driver mutations. Here, we asked to what extent cells acquire and retain an epigenetic memory of the ADM transition state in the absence of oncogene mutation. We identified differential DNA methylation at Kras-downstream PI3K and Rho/Rac/Cdc42 GTPase pathway genes during ADM, as well as a corresponding gene expression increase in these pathways. Importantly, differential methylation persisted after gene expression returned to normal. Caerulein exposure, which induces widespread digestive system changes in addition to ADM, showed similar changes in DNA methylation in ADM cells. Regions of differential methylation were enriched for motifs of KLF and AP-1 family transcription factors, as were those of human pancreatic intraepithelial neoplasia (PanIN) samples, demonstrating the relevance of this epigenetic transition state memory in human carcinogenesis. Finally, single-cell spatial transcriptomics revealed that these ADM transition cells were enriched for PI3K pathway and AP1 family members, linking epigenetic memory to cancer cell plasticity even in the absence of oncogene mutation.

cancer biology↗

Platform-Agnostic CellNet (PACNet) enables cross-study meta-analysis of cell fate engineering protocols

The optimization of cell fate engineering protocols requires evaluating their fidelity, efficiency, or both. We previously adopted CellNet, a computational tool to quantitatively assess the transcriptional fidelity of engineered cells and tissues as compared to their in vivo counterparts based on bulk RNA-Seq. However, this platform and other similar approaches are sensitive to experimental and analytical aspects of transcriptomics methodologies. This makes it challenging to capitalizing on the expansive, publicly available sets of transcriptomic data that reflect the diversity of cell fate engineering protocols. Here, we present Platform-Agnostic CellNet (PACNet), which extends the functionality of CellNet by enabling the assessment of transcriptional profiles in a platform-agnostic manner, and by enabling the comparison of user-supplied data to panels of engineered cell types from state-of-the-art protocols. To demonstrate the utility of PACNet, we evaluated a range of cell fate engineering protocols for cardiomyocytes and hepatocytes. Through this analysis, we identified the best-performing methods, characterized the extent of intra-protocol and inter-lab variation, and identified common off-target signatures, including a surprising neural and neuroendocrine signature in primary liver-derived organoids. Finally, we made our tool accessible as a user-friendly web application that allows users to upload their own transcriptional profiles and assess their protocols relative to our database of reference engineered samples. Highlights* The development of Platform-Agnostic CellNet (PACNet) that classifies engineered cell populations from transcriptome data regardless of profiling method or transcript abundance estimation method * PACNet enables cross-study comparisons of cell fate engineering protocols * Comparison of cardiomyocyte engineering protocols emphasizes metabolic selection as a key step in achieving a strong cardiomyocyte fate. * PACNet identifies an unexpected off-target neural and neuroendocrine signature in primary liver-derived organoids. eTOC BlurbCahan and colleagues created a computational resource, PACNet, which evaluates the fidelity of cell engineering expression profiles in a platform-agnostic manner to facilitate cross-protocol benchmarking. Examining state-of-the-field cardiomyocyte and hepatocyte derivation protocols, they identified that two techniques in cardiomyocyte engineering best increase cardiac identity and that an off-target neural/neuroendocrine signature in primary liver-derived organoids may reflect a cholangiopathic signature. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/506886v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@e4e985org.highwire.dtl.DTLVardef@9f485borg.highwire.dtl.DTLVardef@1475d0borg.highwire.dtl.DTLVardef@2d8c31_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

Radiator: a cloud-based framework for deploying reusable bioinformatics tools

SummaryAccessibility and usability of compute-intensive bioinformatics tools can be increased with simplified web-based graphic user interfaces. However, deploying such tools as web applications presents additional barriers, including the complexity of developing a usable interface, network latency in transferring large datasets, and cost, which we encountered in developing a web-based version of our command-line tool CellNet. Learning and generalizing from this experience, we have devised a lightweight framework, Radiator, to facilitate deploying bioinformatics tools as web applications. To achieve reproducibility, usability, consistent accessibility, throughput, and cost-efficiency, Radiator is designed to be deployed on the cloud. Here, we describe the internals of Radiator and how to use it.\n\nAvailability and ImplementationCode for Radiator and the CellNet Web Application are freely available at https://github.com/pcahan1 under the MIT license. The CellNet WebApp, Radiator, and Radiator-derived applications can be launched through public Amazon Machine Images from the cloud provider Amazon Web Services (AWS) (https://aws.amazon.com/).

bioinformatics↗