Engineering highly active nuclease enzymes with machine learning and high-throughput screening.

Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged fitness landscape and costly experiments. In this work, we present TeleProt, a machine learning (ML) framework that blends evolutionary and experimental data to design diverse protein libraries, and employ it to improve the catalytic activity of a nuclease enzyme that degrades biofilms that accumulate on chronic wounds. After multiple rounds of high-throughput experiments, TeleProt found a significantly better top-performing enzyme than directed evolution (DE), had a better hit rate at finding diverse, high-activity variants, and was even able to design a high-performance initial library using no prior experimental data.

Predicting cancer cell invasion by single-cell physical phenotyping.

The physical properties of cells are promising biomarkers for cancer diagnosis and prognosis. Here we determine the physical phenotypes that best distinguish human cancer cell lines, and their relationship to cell invasion. We use the high throughput, single-cell microfluidic method, quantitative deformability cytometry (q-DC), to measure six physical phenotypes including elastic modulus, cell fluidity, transit time, entry time, cell size, and maximum strain at rates of 102 cells per second.

Quantitative Deformability Cytometry: Rapid, Calibrated Measurements of Cell Mechanical Properties.

Advances in methods that determine cell mechanical phenotype, or mechanotype, have demonstrated the utility of biophysical markers in clinical and research applications ranging from cancer diagnosis to stem cell enrichment. Here, we introduce quantitative deformability cytometry (q-DC), a method for rapid, calibrated, single-cell mechanotyping. We track changes in cell shape as cells deform into microfluidic constrictions, and we calibrate the mechanical stresses using gel beads.

Wrinkling of milk skin is mediated by evaporation.

Wrinkling of thin films and membranes can occur due to various mechanisms such as growth and/or mismatch between the mechanical properties of the film and substrate. However, the physical origins of dynamic wrinkling in soft membranes are still not fully understood. Here we use milk skin as a tractable experimental system to investigate the physics of wrinkle formation in a thin, poroelastic film.

Cancer cells become less deformable and more invasive with activation of β-adrenergic signaling.

Invasion by cancer cells is a crucial step in metastasis. An oversimplified view in the literature is that cancer cells become more deformable as they become more invasive. β-adrenergic receptor (βAR) signaling drives invasion and metastasis, but the effects on cell deformability are not known.

Stiffness of pancreatic cancer cells is associated with increased invasive potential.

Metastasis is a fundamentally physical process in which cells are required to deform through narrow gaps as they invade surrounding tissues and transit to distant sites. In many cancers, more invasive cells are more deformable than less invasive cells, but the extent to which mechanical phenotype, or mechanotype, can predict disease aggressiveness in pancreatic ductal adenocarcinoma (PDAC) remains unclear. Here we investigate the invasive potential and mechanical properties of immortalized PDAC cell lines derived from primary tumors and a secondary metastatic site, as well as noncancerous pancreatic ductal cells.

The physical origins of transit time measurements for rapid, single cell mechanotyping.

The mechanical phenotype or 'mechanotype' of cells is emerging as a potential biomarker for cell types ranging from pluripotent stem cells to cancer cells. Using a microfluidic device, cell mechanotype can be rapidly analyzed by measuring the time required for cells to deform as they flow through constricted channels. While cells typically exhibit deformation timescales, or transit times, on the order of milliseconds to tens of seconds, transit times can span several orders of magnitude and vary from day to day within a population of single cells; this makes it challenging to characterize different cell samples based on transit time data.