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Background Due to continued advances in sequencing technology, the limitation in understanding biological systems through an “-omics” lens is no longer the generation of data, but the ability to analyze it. Importantly, much of this rich -omics data is publicly available waiting to be further investigated. Although many code-based pipelines exist, there is a lack of user-friendly and accessible applications that enable rapid analysis or visualization of data. Results GECO (Gene Expression Clustering Optimization; http://www.theGECOapp.com) is a minimalistic GUI app that utilizes non-linear reduction techniques to rapidly visualize expression trends in many types of biological data matrices (such as bulk RNA-seq or proteomics). The required input is a data matrix with samples and any type of expression level of genes/protein/other with a unique ID. The output is an interactive t-SNE or UMAP analysis that clusters genes (or proteins/other unique IDs) based on their expression patterns across the multiple samples enabling visualization of expression trends. Customizable settings for dimensionality reduction, data normalization, along with visualization parameters including coloring and filters, ensure adaptability to a variety of user uploaded data. Conclusion This local and cloud-hosted web browser app enables investigation of any -omic data matrix in a rapid and code-independent manner. With the continued growth of available -omic data, the ability to quickly evaluate a dataset, including specific genes of interest, is more important than ever. GECO is intended to supplement traditional statistical analysis methods and is particularly useful when visualizing clusters of genes with similar trajectories across many samples (ex: multiple cell types, time course, dose response). Users will be empowered to investigate -omic data with a new lens of visualization and analysis that has the potential to uncover genes of interest, cohorts of co-regulated genes programs, and previously undetected patterns of expression.

BackgroundThere is increasing evidence that oncogenic Wnt signaling directs metabolic reprogramming of cancer cells to favor aerobic glycolysis or Warburg metabolism. In colon cancer, this reprogramming is due to direct regulation of pyruvate dehydrogenase kinase 1 (PDK1) gene transcription. Additional metabolism genes are sensitive to Wnt signaling and exhibit correlative expression with PDK1. Whether these genes are also regulated at the transcriptional level, and therefore a part of a core metabolic gene program targeted by oncogenic WNT signaling, is not known.ResultsHere, we identify monocarboxylate transporter 1 (MCT-1; encoded by SLC16A1) as a direct target gene supporting Wnt-driven Warburg metabolism. We identify and validate Wnt response elements (WREs) in the proximal SLC16A1 promoter and show that they mediate sensitivity to Wnt inhibition via dominant-negative LEF-1 (dnLEF-1) expression and the small molecule Wnt inhibitor XAV939. We also show that WREs function in an independent and additive manner with c-Myc, the only other known oncogenic regulator of SLC16A1 transcription. MCT-1 can export lactate, the byproduct of Warburg metabolism, and it is the essential transporter of pyruvate as well as a glycolysis-targeting cancer drug, 3-bromopyruvate (3-BP). Using sulforhodamine B (SRB) assays to follow cell proliferation, we tested a panel of colon cancer cell lines for sensitivity to 3-BP. We observe that all cell lines are highly sensitive and that reduction of Wnt signaling by XAV939 treatment does not synergize with 3-BP, but instead is protective and promotes rapid recovery.ConclusionsWe conclude that MCT-1 is part of a core Wnt signaling gene program for glycolysis in colon cancer and that modulation of this program could play an important role in shaping sensitivity to drugs that target cancer metabolism.Electronic supplementary materialThe online version of this article (doi:10.1186/s40170-016-0159-3) contains supplementary material, which is available to authorized users.

This article is part of a themed section on WNT Signalling: Mechanisms and Therapeutic Opportunities. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.24/issuetoc.

Intestinal stem cells are non-quiescent, dividing epithelial cells that rapidly differentiate into progenitor cells of the absorptive and secretory cell lineages. The kinetics of this process is rapid such that the epithelium is replaced weekly. To determine how the transcriptome and proteome keep pace with rapid differentiation, we developed a new cell sorting method to purify mouse colon epithelial cells. Here we show that alternative mRNA splicing and polyadenylation dominate changes in the transcriptome as stem cells differentiate into progenitors. In contrast, as progenitors differentiate into mature cell types, changes in mRNA levels dominate the transcriptome. RNA processing targets regulators of cell cycle, RNA, cell adhesion, SUMOylation, and Wnt and Notch signaling. Additionally, global proteome profiling detected >2,800 proteins and revealed RNA:protein patterns of abundance and correlation. Paired together, these data highlight new potentials for autocrine and feedback regulation and provide new insights into cell state transitions in the crypt.

An alarming rise in young onset colorectal cancer (CRC) has been reported; however, the underlying molecular mechanism remains undefined. Suspected risk factors of young onset CRC include environmental aspects, such as lifestyle and dietary factors, which are known to affect the circadian clock. We find that both genetic disruption and environmental disruption of the circadian clock accelerate Apc- driven CRC pathogenesis in vivo. Using an intestinal organoid model, we demonstrate that clock disruption promotes transformation by driving Apc loss of heterozygosity, which hyperactivates Wnt signaling. This up-regulates c-Myc , a known Wnt target, which drives heightened glycolytic metabolism. Using patient-derived organoids, we show that circadian rhythms are lost in human tumors. Last, we identify that variance between core clock and Wnt pathway genes significantly predicts the survival of patients with CRC. Overall, our findings demonstrate a previously unidentified mechanistic link between clock disruption and CRC, which has important implications for young onset cancer prevention.

Objective: To evaluate if patient-derived organoids (PDOs) may predict response to neoadjuvant (NAT) chemotherapy in patients with pancreatic adenocarcinoma. Background: PDOs have been explored as a biomarker of therapy response and for personalized therapeutics in patients with pancreatic cancer. Methods: During 2017–2021, patients were enrolled into an IRB-approved protocol and PDO cultures were established. PDOs of interest were analyzed through a translational pipeline incorporating molecular profiling and drug sensitivity testing. Results: One hundred thirty-six samples, including both surgical resections and fine needle aspiration/biopsy from 117 patients with pancreatic cancer were collected. This biobank included diversity in stage, sex, age, and race, with minority populations representing 1/3 of collected cases (16% Black, 9% Asian, 7% Hispanic/Latino). Among surgical specimens, PDO generation was successful in 71% (15 of 21) of patients who had received NAT prior to sample collection and in 76% (39 of 51) of patients who were untreated with chemotherapy or radiation at the time of collection. Pathological response to NAT correlated with PDO chemotherapy response, particularly oxaliplatin. We demonstrated the feasibility of a rapid PDO drug screen and generated data within 7 days of tissue resection. Conclusion: Herein we report a large single-institution organoid biobank, including ethnic minority samples. The ability to establish PDOs from chemotherapy-naive and post-NAT tissue enables longitudinal PDO generation to assess dynamic chemotherapy sensitivity profiling. PDOs can be rapidly screened and further development of rapid screening may aid in the initial stratification of patients to the most active NAT regimen.

Recent advances in sample preparation enable label-free mass spectrometry (MS)-based proteome profiling of small numbers of mammalian cells. However, specific devices are often required to downscale sample processing volume from the standard 50–200 μL to sub-μL for effective nanoproteomics, which greatly impedes the implementation of current nanoproteomics methods by the proteomics research community. Herein, we report a facile one-pot nanoproteomics method termed SOPs-MS (surfactant-assisted one-pot sample processing at the standard volume coupled with MS) for convenient robust proteome profiling of 50–1000 mammalian cells. Building upon our recent development of SOPs-MS for label-free single-cell proteomics at a low μL volume, we have systematically evaluated its processing volume at 10–200 μL using 100 human cells. The processing volume of 50 μL that is in the range of volume for standard proteomics sample preparation has been selected for easy sample handling with a benchtop micropipette. SOPs-MS allows for reliable label-free quantification of ∼1200–2700 protein groups from 50 to 1000 MCF10A cells. When applied to small subpopulations of mouse colon crypt cells, SOPs-MS has revealed protein signatures between distinct subpopulation cells with identification of ∼1500–2500 protein groups for each subpopulation. SOPs-MS may pave the way for routine deep proteome profiling of small numbers of cells and low-input samples.