Berkeley Lights, Inc.

University of California

Hayley M Bennett

Keith J Breinlinger

John A Tenney

Alexander Marson

Gregory G Lavieu

Theodore Lee Roth

Annamaria Mocciaro

Magali Soumillon

Thomas M Vetterli

Vincent Haw Tien Pai

Darshan Thaker

Ravi K Ramenani

Igor Fastnacht

Yuriy Nicheporuk

Andriy Koval

Khrystyna Andres

Alona Kostenko

Christoph Andreas Neyer

Elisabeth Marie Walczak

Alexander Gerald Olson

Wesley Arthur Zink

Oleksandr Tokmakov

Mckenzi S Toh

Yara X Mejia Gonzales

Methods are described herein for isolating clonal populations of T cells having a defined genetic modification. The methods are performed, at least in part, in a microfluidic device comprising one or more sequestration pens. The methods include the steps of: maintaining individual T cells (or precursors thereof) that have undergone a genomic editing process in corresponding sequestration pens of a microfluidic device; expanding the T cells into respective clonal populations of T cells; detecting, in one or more T cells of each clonal population, the absence of a cell surface marker that was present in the individual T cells (or precursors thereof); and detecting, in one or more T cells of each clonal population, the presence of a first nucleic acid sequence that is indicative of the presence of an on-target genome edit in the clonal population of T cells. Also described are compositions comprising one or more clonal populations of T cells isolated according to the methods disclosed herein.

Methods for selection and generation of genome edited T cells

Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Systems and methods for analyses of biological samples

Growing community of inventors

Emeryville, CA, United States of America

Hayley M Bennett