Box, A. C. | Search bioRxiv

bioRxiv2019

Single cell analysis reveals modified hematopoietic cell composition affecting inflammatory and immunopathological responses in Astyanax mexicanus

Reduced parasite infection rates in the developed world are suspected to underlie the rising prevalence of autoimmune disorders. However, the long-term evolutionary consequences of decreased parasite exposure on an immune system are not well understood. We used the Mexican tetra Astyanax mexicanus to understand how loss of parasite diversity influences the evolutionary trajectory of the vertebrate immune system by comparing river with cave morphotypes. Here, we present field data that affirms a strong reduction in parasite diversity in the cave ecosystem and show that cavefish immune cells display a more sensitive proinflammatory response towards bacterial endotoxins. Surprisingly, other innate cellular immune responses, such as phagocytosis, are drastically decreased in cavefish. Using two independent single-cell approaches, we identified a shift in the overall immune cell composition in cavefish as the underlying cellular mechanism, indicating strong differences in the immune investment strategy. While surface fish invest evenly into the innate and adaptive immune system, cavefish shifted immune investment to the adaptive immune system, and here, mainly towards specific T-cell populations that promote homeostasis. Additionally, inflammatory responses and immunopathological phenotypes in visceral adipose tissue are drastically reduced in cavefish. Our data indicate that long term adaptation to low parasite diversity coincides with a more sensitive immune system in cavefish, which is accompanied by a reduction of the immune cells that play a role in mediating the proinflammatory response.

immunology↗

bioRxiv2019

High-throughput, image-based flow cytometry and clustering method for phenotyping heterogeneous cell populations

Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell cluster pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and to detect changes in cellular composition between different conditions. Therefore, Image3C expands the use of imaged-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available. Impact statementImage3C analyzes cell populations through image-based clustering and neural network training, which allows single-cell analysis in research organisms devoid of species-specific reagents or pre-existing knowledge on cell phenotypes.

zoology↗