Even as we integrate inclusive teaching strategies and course design, the philosophy and implementation of grading continues to be a large source of inequity in higher education. Grades signal to students whether they belong within a course or degree major and dictate access to academic and career opportunities. Consequently, even in a classroom where the instructor shares decision-making power with students, grading ultimately becomes a place where instructors exercise complete power and students exhibit little agency. While efforts have focused on closing the "achievement gap" through teaching practice, the negative impact of grading on students, especially those with marginalized identities, suggests that we must interrogate how the way that we assess students could also contribute to this gap. Innovative approaches to grading have been explored in other settings, yet science and engineering disciplines often rigidly assess student performance through what they consider to be objectively designed and evaluated tasks. This practice and its underlying assumptions have historically done little to help, if not exacerbated existing inequities for students from marginalized identity groups. In this paper, we review the inequities around current practices in grading that motivate the need to change, as well as introduce several frameworks of alternative grading (e.g. contract grading, mastery grading, ungrading). In so doing, we will consider different perspectives on the purpose of grading and what we aim to evaluate in our students through their work. We will examine the importance of merit from the instructor's view and how different interpretations of this ideal affect our students. This paper will also discuss barriers real or perceived to changing how we grade, and propose some strategies for instructors who wish to limit the effects of bias, increase a sense of agency, and ultimately empower students.

Discourse around Science, Technology, Engineering, and Mathematics (STEM) education in the United States has long focused on improving the persistence and academic achievement of students. On the surface, such goals are reasonable and wellintentioned. However, the near-exclusive focus on those two outcomes as shorthand for "success" serves hegemonic norms which preclude the equitable success of all students. Although STEM education research has begun to address the inequitable systems within which students and faculty operate, the language of success has largely not changed. While previous work has aimed to recognize and characterize how normative definitions of success harm students and faculty, they fall short of providing readers with strategies for how to sustainably change these systems of injustice. Utilizing the four frames model for systemic change, this Essay 1) deconstructs the operational definitions of student success among key stakeholders involved in STEM higher education: students, faculty, departments, and institutions; 2) determines how extant policies and practices drive misalignments among these definitions and thwart equity; and 3) highlights three key opportunities for change agents to transform how success is measured and defined within STEM higher education.

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.

Centers for Teaching and Learning (CTLs) are uniquely poised to support instructors engaging in Scholarship of Teaching and Learning (SoTL) through professional expertise in evidence-based teaching practice and dedicated staff resources. Models for this support have ranged from purely a funding source, to learning communities, to one-off technical training and consultations, to comprehensive mentoring and partnerships. In the decade since Schwartz and Haynie published “Faculty Development Centers and the Role of SoTL,” we aimed to profile the current landscape of university CTLs and their involvement in SoTL. In this review, we draw on the multiple models of CTL participation in SoTL developed by Lukes et al. to categorize the work conducted at a sample of American institutions. Using a data mining approach of publicly available information online, we compiled a sample dataset that shows the distribution of CTLs across the US engaging in various forms of SoTL. We examine current trends of CTL and SoTL presence amongst institution types and geographic regions, with consideration for different SoTL program models. We conclude with a discussion of the current landscape of CTLs and their SoTL involvement compared to our aspirations: what will the future of faculty development look like, and what role will SoTL play? Given the pros and cons of each different model for CTL and SoTL integration, is the current distribution of these models as effective as it could be? What changes could lead to greater impact both for CTLs and for SoTL?

The tragic murder of Mr. George Floyd brought to the head long-standing issues of racial justice and equity in the United States and beyond. This prompted many institutions of higher education, including professional organizations and societies, to engage in long-overdue conversations about the role of scientific institutions in perpetuating racism. Similar to many professional societies and organizations, the Society for the Advancement of Biology Education Research (SABER), a leading international professional organization for discipline-based biology education researchers, has long struggled with a lack of representation of People of Color (POC) at all levels within the organization. The events surrounding Mr. Floyd’s death prompted the members of SABER to engage in conversations to promote self-reflection and discussion on how the society could become more antiracist and inclusive. These, in turn, resulted in several initiatives that led to concrete actions to support POC, increase their representation, and amplify their voices within SABER. These initiatives included: a self-study of SABER to determine challenges and identify ways to address them, a year-long seminar series focused on issues of social justice and inclusion, a special interest group to provide networking opportunities for POC and to center their voices, and an increase in the diversity of keynote speakers and seminar topics at SABER conferences. In this article, we chronicle the journey of SABER in its efforts to become more inclusive and antiracist. We are interested in increasing POC representation within our community and seek to bring our resources and scholarship to reimagine professional societies as catalyst agents towards an equitable antiracist experience. Specifically, we describe the 12 concrete actions that SABER enacted over a period of a year and the results from these actions so far. In addition, we discuss remaining challenges and future steps to continue to build a more welcoming, inclusive, and equitable space for all biology education researchers, especially our POC members. Ultimately, we hope that the steps undertaken by SABER will enable many more professional societies to embark on their reflection journeys to further broaden scientific communities.

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.

Abstract Integrated proteomic and metabolic single-cell assays reveal multiple independent adaptive responses to drug tolerance in a BRAF-mutant melanoma cell line Cancers commonly develop resistance against chemotherapeutics or targeted therapies through various types of genetic or non-genetic mechanisms. Non-genetic mechanisms have been shown to occur early on and can provide a latent reservoir of cells for the emergence of various different type of mechanisms, yet very limited understanding of process were resolve main from bulk analysis. Considering the heterogeneous nature of the tumor cells, a single-cell level characterization of the process worth detailed further investigation. Using MAPK inhibition of BRAF-mutant melanomas as a model system, we resolved that cells take different paths to go from drug-sensitive to drug-resistant state. Using a microfludic-based single-cell integrated proteomic and metabolic assay, we assayed for a panel of signaling, phenotypic, and metabolic regulators at four time points during the first five days of drug treatment. Dimensional reduction of the resultant data set, coupled with information theoretic analysis, uncovered a complex cell state landscape and identified two distinct paths connecting drug-naïve and drug-tolerant states. Cells are shown to exclusively traverse one of the two pathways depending on the level of the lineage restricted transcription factor MITF in the drug-naïve cells. The two trajectories are associated with distinct signaling and metabolic susceptibilities, and are independently druggable. Our results update the paradigm of adaptive resistance development in an isogenic cell population and offer insight into the design of more effective combination therapies. Citation Format: Yapeng Su, Guideng Li, Melissa Ko, Hanjun Cheng, Ronghui Zhu, Min Xue, Lidia Robert, Raphael Levine, Antoni Ribas, Garry Nolan, Wei Wei, Sylvia Plevritis, David Baltimore, James R. Heath. Systems biology for investigating drug resistance mechanism of melanoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6585.

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.

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.

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.

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge, with relevance towards understanding biological changes ranging from cellular differentiation to epigenetic (adaptive) responses of diseased cells to drugging. We report on a combined experimental and theoretic method for determining the trajectories that specific highly plastic BRAF V600E mutant patient-derived melanoma cancer cells take between drug-naïve and drug-tolerant states. Recent studies have implicated non-genetic, fast-acting resistance mechanisms are activated in these cells following BRAF inhibition. While single-cell highly multiplex omics tools can yield snapshots of the cell state space landscape sampled at any given time point, individual cell trajectories must be inferred from a kinetic series of snapshots, and that inference can be confounded by stochastic cell state switching. Using a microfludic-based single-cell integrated proteomic and metabolic assay, we assayed for a panel of signaling, phenotypic, and metabolic regulators at four time points during the first five days of drug treatment. Dimensional reduction of the resultant data set, coupled with information theoretic analysis, uncovered a complex cell state landscape and identified two distinct paths connecting drug-naïve and drug-tolerant states. Cells are shown to exclusively traverse one of the two pathways depending on the level of the lineage restricted transcription factor MITF in the drug-naïve cells. The two trajectories are associated with distinct signaling and metabolic susceptibilities, and are independently druggable. Our results update the paradigm of adaptive resistance development in an isogenic cell population and offer insight into the design of more effective combination therapies.

The mutational activation of oncogenes drives cancer development and progression. Classic oncogenes, such asMYCandRAS, 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,CDX2when 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/β-catenin signaling, itself a key oncogenic pathway in colorectal cancer. These data characterizeCDX2as a lineage-survival oncogene deregulated in colorectal cancer. Our findings challenge a prevailing view thatCDX2is a tumor suppressor in colorectal cancer and uncover an additional piece in the multistep model of colorectal tumorigenesis.