Biology subjects

Chambers, S.

Publications and source records attributed to Chambers, S..

3 recordsLinked to original sources

bioRxiv2023

AN ARTIFICIAL INTELLIGENCE FOR RAPID IN-LINE LABEL-FREE HUMAN PLURIPOTENT STEM CELL COUNTING AND QUALITY ASSESSMENT

The current state-of-the-art in hPSC culture is a bespoke and user-dependent process limiting the scale and complexity of the experiments performed and introducing operator-to-operator and day-to-day variation. Artificial intelligence (AI) offers the speed and flexibility to bridge the gap between a human-dependent process and industrial-scale automation. We evaluated an AI approach for counting exact cell numbers of undifferentiated human induced pluripotent stem cells in brightfield images for automating hPSC culture. The neural network generates a topological density map for accurate cell counts. We found that the image-based AI algorithm can determine a precise number of hPSCs and is superior to fluorescence-labeled object detection; the algorithm can ignore well edges, meniscus effects, and dust, achieving an average error of 5.6%. We have built a prototype capable of making a go/no go decision for stem cell passaging to perform 26,400 individual well-level counts from 422,400 images in 12 hours at low cost.

cell biology↗

bioRxiv2022

A Remarkable Adaptive Paradigm Of Heart Performance And Protection Emerges In Response To The Constitutive Challenge Of Marked Cardiac-Specific Overexpression Of Adenylyl Cyclase Type 8

Adult mice with cardiac-specific overexpression of adenylyl cyclase (AC) type VIII (TGAC8) adapt to an incessantly increased cAMP-induced cardiac workload ([~]30% increases in heart rate, ejection fraction and cardiac output) for up to a year without signs of heart failure or excessive mortality. Here we show that despite markedly increased cardiac work, classical cardiac hypertrophy markers were absent in TGAC8, total left ventricular (LV) mass was not increased: a reduced LV cavity volume in TGAC8 was encased by thicker LV walls harboring an increased number of small cardiac myocytes and a network of small interstitial non-cardiac myocytes, manifesting increased proliferation markers and compared to WT. Protein synthesis, proteosome activity, autophagy, and Nrf-2, Hsp90, ACC2 protein levels were increased in TGAC8, but LV ATP and phosphocreatine levels in vivo did not differ by genotype. 2,323 transcripts and 2,184 proteins identified in unbiased omics analyses, spanning a wide array of biological processes and molecular functions in numerous cellular compartments differed in TGAC8 vs WT; and over 250 canonical signaling pathways characteristic of adaptive survival circuitry of cancers, including PI3K and growth factor signaling, cytokine and T cell receptor signaling, immune responses, ROS scavenging, proliferation, protection from apoptosis, and nutrient sensing, were activated in TGAC8; and compared to WT there was a shift from fatty acid oxidation to increased aerobic glycolysis in the context of increased utilization of the pentose phosphate shunt and nucleotide synthesis. Thus, the adaptive paradigm, that becomes activated in the LV of TGAC8 in response to severe chronic, intense AC/PKA/Ca2+ signaling embodies many hallmarks of cancer.

physiology↗

bioRxiv2021

cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices

Quickly and flexibly exploring high-dimensional datasets, such as scRNAseq data, is underserved but critical for hypothesis generation, dataset annotation, publication, sharing, and community reuse. cellxgene is a highly generalizable, web-based interface for exploring high dimensional datasets along categorical, continuous and spatial dimensions, as well as feature annotation. cellxgene is differentiated by its ability to performantly handle millions of observations, and bridges a critical gap by enabling computational and experimental biologists to iteratively ask questions of private and public datasets. In doing so, cellxgene increases the utility and reusability of datasets across the single-cell ecosystem. The codebase can be accessed at https://github.com/chanzuckerberg/cellxgene. For questions and inquiries, please contact cellxgene@chanzuckerberg.com.

systems biology↗