Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.

Background: The purpose of this study was to evaluate the extent to which a longitudinal student-run clinic (SRC) is meeting its stated learning objectives, including providing critical community services and developing physicians who more fully appreciate the social factors affecting their patients’ health. Methods: This was a mixed methods program evaluation of an SRC at Mayo Clinic Alix School of Medicine (MCASOM). A survey was conducted of medical students who had participated in the clinic and seven interviews and three focus groups were conducted with SRC patients, students, faculty, staff, and board members. Transcripts were coded for systematic themes and sub-themes. Major themes were reported. Survey and interview data were integrated by comparing findings and discussing areas of convergence or divergence in order to more fully understand program success and potential areas for improvement. Results: Greater than 85% of student survey respondents (N = 90) agreed or strongly agreed that the SRC met each of its objectives: to provide a vital community service, to explore social determinants of health (SDH), to understand barriers to healthcare access and to practice patience-centered examination. Qualitative data revealed that the SRC contextualized authentic patient care experiences early in students’ medical school careers, but the depth of learning was variable between students. Furthermore, exposure to SDH through the program did not necessarily translate to student understanding of the impact of these social factors on patient’s health nor did it clearly influence students’ future practice goals. Conclusions: The MCASOM SRC experience met core learning objectives, but opportunities to improve long-term impact on students were identified. Participation in the SRC enabled students to engage in patient care early in training that is representative of future practices. SRCs are an avenue by which students can gain exposure to real-world applications of SDH and barriers to healthcare access, but additional focus on faculty development and intentional reflection may be needed to translate this exposure to actionable student understanding of social factors that impact patient care.

Anticipating and understanding cancers’ need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors’ dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.