Optimal marker gene selection for cell type discrimination in single cell analyses.

Single-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods. This results in a substantially more robust and less redundant set of markers than existing methods, most of which identify markers that separate each cell label from the rest. When applied to a data set given a hierarchy of cell types as labels, the markers found by our method improves the recovery of the cell type hierarchy with fewer markers than existing methods using a computationally efficient and principled optimization.

Images as occlusions of textures: a framework for segmentation.

We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that local histograms are a useful tool for segmenting them. Based on our theoretical results, we describe a flexible segmentation framework that draws on existing work on nonnegative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method.

In vitro investigation of the biological effects associated with human dermal fibroblasts exposed to 2.52 THz radiation.

BACKGROUND: Terahertz (THz) radiation sources are increasingly being used in military, defense, and medical applications. However, the biological effects associated with this type of radiation are not well characterized. In this study, we evaluated the cellular and molecular response of human dermal fibroblasts exposed to THz radiation. METHODS: In vitro exposures were performed in a temperature-controlled chamber using a molecular gas THz laser (2.52 THz, 84.8 mW cm(-2), durations: 5, 10, 20, 40, or 80 minutes). Both computational and empirical dosimetric techniques were conducted using finite-difference time-domain (FDTD) modeling approaches, infrared cameras, and thermocouples. Cellular viability was assessed using conventional MTT assays. In addition, the transcriptional activation of protein and DNA sensing genes were evaluated using qPCR. Comparable analyses were also conducted for hyperthermic and genotoxic positive controls. RESULTS: We found that cellular temperatures increased by 3°C during all THz exposures. We also found that for each exposure duration tested, the THz and hyperthermic exposure groups exhibited equivalent levels of cell survival (≥90%) and heat shock protein expression (∼3.5-fold increases). In addition, the expression of DNA sensing and repair genes was unchanged in both groups; however, appreciable increases were observed in the genotoxic controls. CONCLUSIONS: Human dermal fibroblasts exhibit comparable cellular and molecular effects when exposed to THz radiation and hyperthermic stress. These findings suggest that radiation at 2.52 THz generates primarily thermal effects in mammalian cells. Therefore, we conclude that THz-induced bioeffects may be accurately predicted with conventional thermal damage models.

Plasma membrane permeabilization by trains of ultrashort electric pulses.

Ultrashort electric pulses (USEP) cause long-lasting increase of cell membrane electrical conductance, and that a single USEP increased cell membrane electrical conductance proportionally to the absorbed dose (AD) with a threshold of about 10 mJ/g. The present study extends quantification of the membrane permeabilization effect to multiple USEP and employed a more accurate protocol that identified USEP effect as the difference between post- and pre-exposure conductance values (Deltag) in individual cells. We showed that Deltag can be increased by either increasing the number of pulses at a constant E-field, or by increasing the E-field at a constant number of pulses. For 60-ns pulses, an E-field threshold of 6 kV/cm for a single pulse was lowered to less than 1.7 kV/cm by applying 100-pulse or longer trains. However, the reduction of the E-field threshold was only achieved at the expense of a higher AD compared to a single pulse exposure. Furthermore, the effect of multiple pulses was not fully determined by AD, suggesting that cells permeabilized by the first pulse(s) in the train become less vulnerable to subsequent pulses. This explanation was corroborated by a model that treated multiple-pulse exposures as a series of single-pulse exposures and assumed an exponential decline of cell susceptibility to USEP as Deltag increased after each pulse during the course of the train.