Side-by-side comparison of AI visibility scores, market position, and capabilities
Leading single-cell genomics platform; $642M FY2025 revenue. Chromium and Visium spatial platforms power cell-type discovery in oncology, neuroscience, and immunology.
10x Genomics was founded in 2012 in Pleasanton, California by Serge Saxonov, Ben Hindson, and Kevin Ness. The company developed the Chromium platform for single-cell RNA sequencing (scRNA-seq), enabling researchers to analyze gene expression across thousands of individual cells simultaneously—a capability that transformed cell biology, immunology, and cancer research. The Visium Spatial Gene Expression platform extended this to map gene expression within intact tissue architecture.\n\n10x Genomics' instruments and consumables are used by leading academic research centers, pharmaceutical companies, and translational medicine teams to profile the cellular composition of tumors, map the nervous system, characterize immune responses, and develop cell therapies. The company reported full-year 2025 revenue of $642.8 million, up from $610.8 million in 2024, and guides to $600–$625 million for 2026 amid cautious biotech funding conditions.\n\nThe company is transitioning toward a broader multi-omics portfolio including ATAC-seq, protein detection, and spatial multi-omics, while defending market position against competition from Parse Biosciences, Vizgen, and others. Despite near-term funding headwinds in academic markets, 10x Genomics remains a foundational infrastructure provider for the cell atlas era of biology.
a2z Radiology AI raised $20M in 2025 for its whole-body AI that simultaneously screens for 24+ conditions across CT scans — from incidental cancers to cardiovascular risk — in a single automated read.
a2z Radiology AI has developed a whole-body CT analysis platform that simultaneously screens for over 24 medical conditions across a single CT scan, including incidental cancers, coronary artery disease, aortic aneurysm, bone density loss, and organ abnormalities. The AI acts as a second reader that radiologists can use to catch incidental findings that fall outside the primary reason for a scan — a major source of missed diagnoses.
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