An artificial intelligence-assisted diagnostic platform for rapid near-patient hematology.

Hematology analyzers capable of performing complete blood count (CBC) have lagged in their prevalence at the point-of-care. Sight OLO (Sight Diagnostics, Israel) is a novel hematological platform which provides a 19-parameter, five-part differential CBC, and is designed to address the limitations in current point-of-care hematology analyzers using recent advances in artificial intelligence (AI) and computer vision. Accuracy, repeatability, and flagging capabilities of OLO were compared with the Sysmex XN-Series System (Sysmex, Japan). Matrix studies compared performance using venous, capillary and direct-from-fingerprick blood samples. Regression analysis shows strong concordance between OLO and the Sysmex XN, demonstrating that OLO performs with high accuracy for all CBC parameters. High repeatability and reproducibility were demonstrated for most of the testing parameters. The analytical performance of the OLO hematology analyzer was validated in a multicenter clinical laboratory setting, demonstrating its accuracy and comparability to clinical laboratory-based hematology analyzers. Furthermore, the study demonstrated the validity of CBC analysis of samples collected directly from fingerpricks.

Differential regulation of OCT4 targets facilitates reacquisition of pluripotency.

Ectopic transcription factor expression enables reprogramming of somatic cells to pluripotency, albeit with generally low efficiency. Despite steady progress in the field, the exact molecular mechanisms that coordinate this remarkable transition still remain largely elusive. To better characterize the final steps of pluripotency induction, we optimized an experimental system where pluripotent stem cells are differentiated for set intervals before being reintroduced to pluripotency-supporting conditions. Using this approach, we identify a transient period of high-efficiency reprogramming where ectopic transcription factors, but not serum/LIF alone, rapidly revert cells to pluripotency with near 100% efficiency. After this period, cells reprogram with somatic-like kinetics and efficiencies. We identify a set of OCT4 bound cis-regulatory elements that are dynamically regulated during this transient phase and appear central to facilitating reprogramming. Interestingly, these regions remain hypomethylated during in vitro and in vivo differentiation, which may allow them to act as primary targets of ectopically induced factors during somatic cell reprogramming.

Control of relative timing and stoichiometry by a master regulator.

Developmental processes in cells require a series of complex steps. Often only a single master regulator activates genes in these different steps. This poses several challenges: some targets need to be ordered temporally, while co-functional targets may need to be synchronized in both time and expression level. Here we study in single cells the dynamic activation patterns of early meiosis genes in budding yeast, targets of the meiosis master regulator Ime1. We quantify the individual roles of the promoter and protein levels in expression pattern control, as well as the roles of individual promoter elements. We find a consistent expression pattern difference between a non-cofunctional pair of genes, and a highly synchronized activation of a co-functional pair. We show that dynamic control leading to these patterns is distributed between promoter, gene and external regions. Through specific reciprocal changes to the promoters of pairs of genes, we show that different genes can use different promoter elements to reach near identical activation patterns.

Event timing at the single-cell level.

The timing of a cellular event often hides critical information on the process leading to the event. Our ability to measure event times in single cells along with other quantities allow us to learn about the drivers of the timed process and its downstream effects. In this review, we cover different types of events that have been timed in single cells, methods to time such events and types of analysis that have been applied to event timings. We show how different timing distributions suggest different natures for the process. The statistical relations between the timing of different events may reveal how their respective processes are related biologically: Do they occur in sequence or in parallel? Are they independent or inter-dependent? Finally, quantifying morphological and molecular variables may help assess their contribution to the timing of an event and its related process.