Berkeley Lights, Inc.

Bruker Cellular Analysic, Inc.

Vincent Haw Tien Pai

Thomas M Vetterli

Keith J Breinlinger

John A Tenney

Peter J Beemiller

Natalie C Marks

Guido K Stadler

Yelena Bronevetsky

Hansohl E Kim

Hayley M Bennett

Darshan Thaker

Alona Kostenko

Elisabeth Marie Walczak

Khrystyna Andres

Jason M Mcewen

Wesley Arthur Zink

Amanda L Goodsell

Annamaria Mocciaro

Alexander Gerald Olson

Duane Smith

Igor Fastnacht

Oleksandr Tokmakov

Yuriy Nicheporuk

Christoph Andreas Neyer

Andriy Koval

Methods are provided for the assay of secreted biomolecules using automated detection and characterization of micro-objects in a microfluidic device. The biomolecules can be secreted by cells, particularly immunological cells, such as T cells. The biomolecules being assayed can include cytokines, growth factors, and the like. Methods are also provided for assaying the cytotoxicity of a cell with respect to another, target cell. Also provided are kits and non-transitory computer-readable media in which programs are stored for causing a system comprising a computer to perform automated methods for detecting secreted biomolecules and/or cytotoxicity in a microfluidic device.

Methods for assaying biological cells in a microfluidic device

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

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Emeryville, CA, United States of America

Vincent Haw Tien Pai