Work Flows for Cellular Epidemiology, From Conception to Translation

Changes in cell connectivity and morphology, observed and measured using microscopy, implicate a cellular basis of degenerative disease in tissues as diverse as bone, kidney and brain. To date, limitations inherent to sampling (biopsy sites) and/or microscopy (trade-offs between regions of interest and image resolution) have prevented early identification of cellular changes in specimen sizes of diagnostic relevance for human anatomy and physiology. This manuscript describes work flows for human tissue-based cell epidemiology studies. Using recently published sample preparation methods, developed and validated to maximize imaging quality, the largest-to-date scanning electron microscopy map was created showing cellular connections in the femoral neck of a human hip. The map, from a patient undergoing hip replacement, comprises an 11 TB dataset including over 7 million electron microscopy images. This map served as a test case to implement machine learning algorithms for automated detection of cells and identification of their health state. The test case showed a significant link between cell connectivity and health state in osteocytes of the human femur. Combining new, rapid throughput electron microscopy methods with machine learning approaches provides a basis for assessment of cell population health at nanoscopic resolution and in mesoscopic tissue and organ samples. This sets a path for next generation cellular epidemiology, tracking outbreaks of disease in populations of cells that inhabit tissues and organs within individuals.