Poster
Using machine learning to optimize assays for single-cell targeted DNA sequencing
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 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
EHA (2024)
Single-Cell MRD Assessment in AML Reveals Clonal Diversity and Genotype–Phenotype Discordance Missed by Bulk Methods
Adam Sciambi, Daniel Mendoza, Kathryn Thompson, Lan W. Beppu, Benjamin Geller, Indira Krishnan, Lubna Nousheen, Shu Wang, Charlie Murphy, Jerald P. Radich, and Todd E. Druley
(2025)
Comprehensive On-and Off-target Confirmation Using Integrated rhAmpSeq and Targeted DNA Sequencing Single-Cell Technology
Indira Krishnan, Shu Wang, Saurabh Gulati
ASGCT 2025 (2025)
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
ESGCT 2024 (2024)