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Using machine learning to optimize assays for single-cell targeted DNA sequencing

Nianzhen Li

High-throughput single-cell DNA sequencing allows for the detection of rare mutations in cells and identification of sub-clones defined by co-occurrence of mutations. The big challenge with multiplex sequencing at the single-cell level is the non-uniform amplification that results in inadequate coverage of mutations of interest.

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Single-cell Multi-omics Analysis Reveals Differential Lineage-specific Vector Copy Number Distribution in CAR-T Cell Products

Yilong Yang; Saurabh Parikh; Khushali Patel; Qawer Ayaz; Lindsey Murphy; Amanda Winters; Terry J. Fry; Mahir Mohiuddin; Hua-Jun He; John Elliott; Benjamin Schroeder; Shu Wang; Chieh-Yuan (Alex) Li

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High Throughput Single-cell Assessment of Genome Integrity and Toxicity Events Associated With Edited Cells

Chieh-Yuan (Alex) Li; Saurabh Parikh; Saurabh Gulati; Donjo Ban; Nechama Kalter; Michael Rosenburg; Qawer Ayaz; Joanne Nguyen; Benjamin Miltz; Yang Li1; Madhumita Shrikhande; Edward Szekeres; Ayal Hendel; Benjamin Schroeder; Shu Wang

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Single-cell Multiomic Clonal Tracking in Myeloma Identifies SMM Clones that Progress to MM and Low-Frequency MM Clones with Resistance Features Enabling More Precise Application of Targeted Therapies

Adam Sciambi

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A novel single-cell measurable residual disease (scMRD) assay for simultaneous DNA mutation and surface immunophenotype profiling

Holly Tillson

Using machine learning to optimize assays for single-cell targeted DNA sequencing

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