A stochastic model of blood flow to calculate blood dose during radiotherapy.

Purpose. Lymphopenia is a common side effect in patients treated with radiotherapy, potentially caused by direct cell killing of circulating lymphocytes in the blood. To investigate this hypothesis, a method to assess dose to circulating lymphocytes is needed.Methods. A stochastic model to simulate systemic blood flow in the human body was developed based on a previously designed compartment model. Blood dose was obtained by superimposing the spatiotemporal distribution of blood particles with a time-varying dose rate field, and used as a surrogate for dose to circulating lymphocytes. We discuss relevant theory on compartmental modeling and how to combine it with models of explicit organ vasculature.Results. A general workflow was established which can be used for any anatomical site. Stochastic compartments can be replaced by explicit models of organ vasculatures for improved spatial resolution, and tumor compartments can be dynamically assigned. Generating a patient-specific blood flow distribution takes about one minute, fast enough to investigate the effect of varying treatment parameters such as the dose rate. Furthermore, the anatomical structures contributing most to the overall blood dose can be identified, which could potentially be used for lymphocyte-sparing treatment planning.Conclusion. The ability to report the blood dose distribution during radiotherapy is imperative to test and act upon the current paradigm that radiation-induced lymphopenia is caused by direct cell killing of lymphocytes in the blood. We have built a general model that can do so for various treatment sites. The presented framework is publicly available athttp://github.com/mghro/hedos.

A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection.

We propose 'Tapestry', a single-round pooled testing method with application to COVID-19 testing using quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can result in shorter testing time and conservation of reagents and testing kits, at clinically acceptable false positive or false negative rates. Tapestry combines ideas from compressed sensing and combinatorial group testing to create a new kind of algorithm that is very effective in deconvoluting pooled tests. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test and the output is a list of viral loads for each sample relative to the pool with the highest viral load. For guaranteed recovery of [Formula: see text] infected samples out of [Formula: see text] being tested, Tapestry needs only [Formula: see text] tests with high probability, using random binary pooling matrices. However, we propose deterministic binary pooling matrices based on combinatorial design ideas of Kirkman Triple Systems, which balance between good reconstruction properties and matrix sparsity for ease of pooling while requiring fewer tests in practice. This enables large savings using Tapestry at low prevalence rates while maintaining viability at prevalence rates as high as 9.5%. Empirically we find that single-round Tapestry pooling improves over two-round Dorfman pooling by almost a factor of 2 in the number of tests required. We evaluate Tapestry in simulations with synthetic data obtained using a novel noise model for RT-PCR, and validate it in wet lab experiments with oligomers in quantitative RT-PCR assays. Lastly, we describe use-case scenarios for deployment.

High-throughput gene screen reveals modulators of nuclear shape.

Irregular nuclear shapes characterized by blebs, lobules, micronuclei, or invaginations are hallmarks of many cancers and human pathologies. Despite the correlation between abnormal nuclear shape and human pathologies, the mechanism by which the cancer nucleus becomes misshapen is not fully understood. Motivated by recent evidence that modifying chromatin condensation can change nuclear morphology, we conducted a high-throughput RNAi screen to identify epigenetic regulators that are required to maintain normal nuclear shape in human breast epithelial MCF-10A cells. We silenced 608 genes in parallel using an epigenetics siRNA library and used an unbiased Fourier analysis approach to quantify nuclear contour irregularity from fluorescent images captured on a high-content microscope. Using this quantitative approach, which we validated with confocal microscopy, we significantly expand the list of epigenetic regulators that impact nuclear morphology.

Apical cell protrusions cause vertical deformation of the soft cancer nucleus.

Breast cancer nuclei have highly irregular shapes, which are diagnostic and prognostic markers of breast cancer progression. The mechanisms by which irregular cancer nuclear shapes develop are not well understood. Here we report the existence of vertical, apical cell protrusions in cultured MDA-MB-231 breast cancer cells. Once formed, these protrusions persist over time scales of hours and are associated with vertically upward nuclear deformations. They are absent in normal mammary epithelial cells (MCF-10A cells). Microtubule disruption enriched these protrusions preferentially in MDA-MB-231 cells compared with MCF-10A cells, whereas inhibition of nonmuscle myosin II (NMMII) abolished this enrichment. Dynamic confocal imaging of the vertical cell and nuclear shape revealed that the apical cell protrusions form first, and in response, the nucleus deforms and/or subsequently gets vertically extruded into the apical protrusion. Overexpression of lamin A/C in MDA-MB-231 cells reduced nuclear deformation in apical protrusions. These data highlight the role of mechanical stresses generated by moving boundaries, as well as abnormal nuclear mechanics in the development of abnormal nuclear shapes in breast cancer cells.