Counting viable cells is a universal practice in microbiology. The colony-forming unit (CFU) assay has remained the gold standard to measure viability across disciplines, but it is time-intensive and resource-consuming. Here we describe the geometric viability assay (GVA) that replicates CFU measurements over 6 orders of magnitude while reducing over 10-fold the time and consumables required. GVA computes a sample’s viable cell count on the basis of the distribution of embedded colonies growing inside a pipette tip. GVA is compatible with Gram-positive and Gram-negative planktonic bacteria (Escherichia coli, Pseudomonas aeruginosa and Bacillus subtilis), biofilms and fungi (Saccharomyces cerevisiae). Laborious CFU experiments such as checkerboard assays, treatment time-courses and drug screens against slow-growing cells are simplified by GVA. The ease and low cost of GVA evinces that it can replace existing viability assays and enable viability measurements at previously impractical scales.

Dysregulated metabolism can broadly affect therapy resistance by influencing compensatory signaling and expanding proliferation. Given many BRAF-mutated melanoma patients experience disease progression with targeted BRAF inhibitors, we hypothesized therapeutic response is related to tumor metabolic phenotype, and that altering tumor metabolism could change therapeutic outcome. We demonstrated the proliferative kinetics of BRAF-mutated melanoma cells treated with the BRAF inhibitor PLX4720 fall along a spectrum of sensitivity, providing a model system to study the interplay of metabolism and drug sensitivity. We discovered an inverse relationship between glucose availability and sensitivity to BRAF inhibition through characterization of metabolic phenotypes using nearly a dozen metabolic parameters in Principle Component Analysis. Subsequently, we generated rho0 variants that lacked functional mitochondrial respiration and increased glycolytic metabolism. The rho0 cell lines exhibited increased sensitivity to PLX4720 compared to the respiration-competent parental lines. Finally, we utilized the FDA-approved antiretroviral drug zalcitabine to suppress mitochondrial respiration and to force glycolysis in our cell line panel, resulting in increased PLX4720 sensitivity via shifts in EC50 and Hill slope metrics. Our data suggest that forcing tumor glycolysis in melanoma using zalcitabine or other similar approaches may be an adjunct to increase the efficacy of targeted BRAF therapy.

Activating mutation of the KRAS gene is common in some cancers, such as pancreatic cancer, but rare in other cancers. Chronic pancreatitis is a predisposing condition for pancreatic ductal adenocarcinoma (PDAC), but how it synergizes with KRAS mutation is not known. We used a mouse model to express an activating mutation of Kras in conjunction with obstruction of the main pancreatic duct to recapitulate a common etiology of human chronic pancreatitis. Because the cell of origin of PDAC is not clear, Kras mutation was introduced into either duct cells or acinar cells. Although KrasG12D expression in both cell types was protective against damage-associated cell death, chronic pancreatitis induced p53, p21, and growth arrest only in acinar-derived cells. Mutant duct cells did not elevate p53 or p21 expression and exhibited increased proliferation driving the appearance of PDAC over time. One mechanism by which tissues may be susceptible or resistant to KRASG12D-initiated tumorigenesis is whether they undergo a p53-mediated damage response. In summary, we have uncovered a mechanism by which inflammation and intrinsic cellular programming synergize for the development of PDAC.

WD40 Repeat Domain 5 (WDR5) is a highly conserved nuclear protein that recruits MYC oncoprotein transcription factors to chromatin to stimulate ribosomal protein gene expression. WDR5 is tethered to chromatin via an arginine-binding cavity known as the “WIN” site. Multiple pharmacological inhibitors of the WDR5-interaction site of WDR5 (WINi) have been described, including those with picomolar affinity and oral bioavailability in mice. Thus far, however, WINi have only been shown to be effective against a number of rare cancer types retaining wild-type p53. To explore the full potential of WINi for cancer therapy, we systematically profiled WINi across a panel of cancer cells, alone and in combination with other agents. We report that WINi are unexpectedly active against cells derived from both solid and blood-borne cancers, including those with mutant p53. Among hematologic malignancies, we find that WINi are effective as a single agent against leukemia and diffuse large B cell lymphoma xenograft models, and can be combined with the approved drug venetoclax to suppress disseminated acute myeloid leukemia in vivo. These studies reveal actionable strategies for the application of WINi to treat blood-borne cancers and forecast expanded utility of WINi against other cancer types.

Changes in bacterial physiology necessarily precede cell death in response to antibiotics. Herein we investigate the early disruption of Ca²⁺ homeostasis as a marker for antibiotic response. Using a machine learning framework, we quantify the temporal information encoded in single-cell Ca²⁺ dynamics. We find Ca²⁺ dynamics distinguish kanamycin sensitive and resistant cells before changes in gross cell phenotypes such as cell growth or protein stability. The onset time (pharmacokinetics) and probability (pharmacodynamics) of these aberrant Ca²⁺ dynamics are dose and time-dependent, even at the resolution of single-cells. Of the compounds profiled, we find Ca²⁺ dynamics are also an indicator of Polymyxin B activity. In Polymyxin B treated cells, we find aberrant Ca²⁺ dynamics precedes the entry of propidium iodide marking membrane permeabilization. Additionally, we find modifying membrane voltage and external Ca²⁺ concentration alters the time between these aberrant dynamics and membrane breakdown suggesting a previously unappreciated role of Ca²⁺ in the membrane destabilization during Polymyxin B treatment. In conclusion, leveraging live, single-cell, Ca²⁺ imaging coupled with machine learning, we have demonstrated the discriminative capacity of Ca²⁺ dynamics in identifying antibiotic-resistant bacteria.