Broadening Participation in Biology Education Research: A role for affinity groups in promoting social connectivity, self-efficacy, and belonging.

Discipline-based education research (DBER) has experienced dramatic growth over recent years, but with growth comes concerns about whether DBER efforts accurately represent the education landscape. By many measures, DBER does not feature a representative range of institutional contexts or a diverse array of voices. Numerous professional development efforts have sought to broaden DBER participation. However, few studies investigate factors that increase engagement by individuals from underrepresented contexts. Drawing on theory related to belonging, self-efficacy, and social learning communities, we investigated persistence in an affinity group aimed at engaging community college faculty (CCF) in biology education research (BER). CCF and CC contexts are dramatically underrepresented in BER in comparison to their central positioning in higher education. We conducted a 4-y study of CCF participants' sense of belonging, self-efficacy, and network connectivity. Our results suggest a relationship between social connectivity, belonging, and persistence in the community, indicating an increase of either of these factors may increase persistence. Self-efficacy increased alongside belonging within the affinity group, which correlated with belonging in BER broadly. These results might inform efforts to engage underrepresented groups of DBER scholars and suggest that such efforts go beyond provision of resources and skills, to focus on building social connections.

Undergraduate Biology Education Research Gordon Research Conference: A Meeting Report.

The 2019 Undergraduate Biology Education Research Gordon Research Conference (UBER GRC), titled "Achieving Widespread Improvement in Undergraduate Education," brought together a diverse group of researchers and practitioners working to identify, promote, and understand widespread adoption of evidence-based teaching, learning, and success strategies in undergraduate biology. Graduate students and postdocs had the additional opportunity to present and discuss research during a Gordon Research Seminar (GRS) that preceded the GRC. This report provides a broad overview of the UBER GRC and GRS and highlights major themes that cut across invited talks, poster presentations, and informal discussions. Such themes include the importance of working in teams at multiple levels to achieve instructional improvement, the potential to use big data and analytics to inform instructional change, the need to customize change initiatives, and the importance of psychosocial supports in improving undergraduate student well-being and academic success. The report also discusses the future of the UBER GRC as an established meeting and describes aspects of the conference that make it unique, both in terms of facilitating dissemination of research and providing a welcoming environment for conferees.

Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line.

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.

Deep profiling of apoptotic pathways with mass cytometry identifies a synergistic drug combination for killing myeloma cells.

Multiple myeloma is an incurable and fatal cancer of immunoglobulin-secreting plasma cells. Most conventional therapies aim to induce apoptosis in myeloma cells but resistance to these drugs often arises and drives relapse. In this study, we sought to identify the best adjunct targets to kill myeloma cells resistant to conventional therapies using deep profiling by mass cytometry (CyTOF). We validated probes to simultaneously detect 26 regulators of cell death, mitosis, cell signaling, and cancer-related pathways at the single-cell level following treatment of myeloma cells with dexamethasone or bortezomib. Time-resolved visualization algorithms and machine learning random forest models (RFMs) delineated putative cell death trajectories and a hierarchy of parameters that specified myeloma cell survival versus apoptosis following treatment. Among these parameters, increased amounts of phosphorylated cAMP response element-binding protein (CREB) and the pro-survival protein, MCL-1, were defining features of cells surviving drug treatment. Importantly, the RFM prediction that the combination of an MCL-1 inhibitor with dexamethasone would elicit potent, synergistic killing of myeloma cells was validated in other cell lines, in vivo preclinical models and primary myeloma samples from patients. Furthermore, CyTOF analysis of patient bone marrow cells clearly identified myeloma cells and their key cell survival features. This study demonstrates the utility of CyTOF profiling at the single-cell level to identify clinically relevant drug combinations and tracking of patient responses for future clinical trials.

FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets.

High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.

CDX2 is an amplified lineage-survival oncogene in colorectal cancer.

The mutational activation of oncogenes drives cancer development and progression. Classic oncogenes, such as MYC and RAS, are active across many different cancer types. In contrast, "lineage-survival" oncogenes represent a distinct and emerging class typically comprising transcriptional regulators of a specific cell lineage that, when deregulated, support the proliferation and survival of cancers derived from that lineage. Here, in a large collection of colorectal cancer cell lines and tumors, we identify recurrent amplification of chromosome 13, an alteration highly restricted to colorectal-derived cancers. A minimal region of amplification on 13q12.2 pinpoints caudal type homeobox transcription factor 2 (CDX2), a regulator of normal intestinal lineage development and differentiation, as a target of the amplification. In contrast to its described role as a colorectal tumor suppressor, CDX2 when amplified is required for the proliferation and survival of colorectal cancer cells. Further, transcriptional profiling, binding-site analysis, and functional studies link CDX2 to Wnt/ß-catenin signaling, itself a key oncogenic pathway in colorectal cancer. These data characterize CDX2 as a lineage-survival oncogene deregulated in colorectal cancer. Our findings challenge a prevailing view that CDX2 is a tumor suppressor in colorectal cancer and uncover an additional piece in the multistep model of colorectal tumorigenesis.