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Recent advances in gene editing are enabling the engineering of cells with an unprecedented level of scale. To capitalize on this opportunity, new methods are needed to accelerate the different steps required to manufacture and handle engineered cells. Here, we describe the development of an integrated software and hardware platform to automate Fluorescence-Activated Cell Sorting (FACS), a central step for the selection of cells displaying desired molecular attributes. Sorting large numbers of samples is laborious, and, to date, no automated system exists to sequentially manage FACS samples, likely owing to the need to tailor sorting conditions (“gating”) to each individual sample. Our platform is built around a commercial instrument and integrates the handling and transfer of samples to and from the instrument, autonomous control of the instrument’s software, and the algorithmic generation of sorting gates, resulting in walkaway functionality. Automation eliminates operator errors, standardizes gating conditions by eliminating operator-to-operator variations, and reduces hands-on labor by 93%. Moreover, our strategy for automating the operation of a commercial instrument control software in the absence of an Application Program Interface (API) exemplifies a universal solution for other instruments that lack an API. Our software and hardware designs are fully open-source and include step-by-step build documentation to contribute to a growing open ecosystem of tools for high-throughput cell biology.

Tissue culture in 96-well microplates is conventionally a tedious, highly manual process sensitive to individual technique and experimenter error. Here, we describe the Automated Cell Culture Splitter, a system for passaging plates of adherent or suspension cells, for routine culture maintenance or specialized applications such as seeding plates for microscopy. The system is built around the Opentrons OT-2 liquid handling robot and incorporates a novel on-deck imaging-based cell counter which allows it to compensate for density disparities across a source plate and control the number of cells seeded on a per-well basis. We find this solution can cut hands-on time by 61% and the results compare favorably to our existing manual cell culture processes in terms of both seeding density precision and biological outcomes, achieving a control of seeding density with a well-to-well coefficient of variation under 11%. The system is designed to be adaptable and an accessible entry point into automation for high-throughput cell culture; to that end, all of the source code and hardware designs are released under open source licenses.

About the Authors: Emily D. Crawford Affiliations Chan Zuckerberg Biohub, San Francisco, California, United States of America, University of California San Francisco, Department of Microbiology and Immunology, San Francisco, California, United States of America Irene Acosta Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Vida Ahyong Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Erika C. Anderson Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Shaun Arevalo Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Daniel Asarnow Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Shannon Axelrod Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Patrick Ayscue Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Camillia S. Azimi Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Caleigh M. Azumaya Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Stefanie Bachl Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Iris Bachmutsky Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Aparna Bhaduri Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Jeremy Bancroft Brown Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Joshua Batson Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Astrid Behnert Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Ryan M. Boileau Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Saumya R. Bollam Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Alain R. Bonny Affiliation: University of California San Francisco, Department of Biochemistry and Biophysics, San Francisco, California, United States of America David Booth Affiliation: University of California San Francisco, Department of Biochemistry and Biophysics, San Francisco, California, United States of America Michael Jerico B. Borja Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America David Brown Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Bryan Buie Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Cassandra E. Burnett Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Lauren E. Byrnes Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Katelyn A. Cabral Affiliations University of California San Francisco, School of Medicine, San Francisco, California, United States of America, University of California San Francisco, Institute for Neurodegenerative Diseases, San Francisco, California, United States of America Joana P. Cabrera Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Saharai Caldera Affiliations Chan Zuckerberg Biohub, San Francisco, California, United States of America, University of California San Francisco, Division of Infectious Disease, San Francisco, California, United States of America Gabriela Canales Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Gloria R. Castañeda Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Agnes Protacio Chan Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Christopher R. Chang Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Arthur Charles-Orszag Affiliations University of California San Francisco, School of Medicine, San Francisco, California, United States of America, Howard Hughes Medical Institute, Chevy Chase, Maryland, United States of America Carly Cheung Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Unseng Chio Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Eric D. Chow Affiliation: University of California San Francisco, Department of Biochemistry and Biophysics, San Francisco, California, United States of America Y. Rose Citron Affiliation: University of California, Berkeley, California, United States of America Allison Cohen Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Lillian B. Cohn Affiliations Chan Zuckerberg Biohub, San Francisco, California, United States of America, University of California San Francisco, Department of Experimental Medicine, San Francisco, California, United States of America Charles Chiu Affiliation: University of California San Francisco, Department of Laboratory Medicine, San Francisco, California, United States of America Mitchel A. Cole Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Daniel N. Conrad Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Angela Constantino Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Andrew Cote Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Tre’Jon Crayton-Hall Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Spyros Darmanis Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Angela M. Detweiler Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Rebekah L. Dial Affiliation: University of California San Francisco, Department of Biochemistry and Biophysics, San Francisco, California, United States of America Shen Dong Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Elias M. Duarte Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America David Dynerman Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Rebecca Egger Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Alison Fanton Affiliation: University of California, Berkeley, California, United States of America Stacey M. Frumm Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Becky Xu Hua Fu Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Valentina E. Garcia Affiliation: University of California San Francisco, Department of Biochemistry and Biophysics, San Francisco, California, United States of America Julie Garcia Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Christina Gladkova Affiliations University of California San Francisco, School of Medicine, San Francisco, California, United States of America, Howard Hughes Medical Institute, Chevy Chase, Maryland, United States of America Miriam Goldman Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Rafael Gomez-Sjoberg Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America M. Grace Gordon Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America James C. R. Grove Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Shweta Gupta Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Alexis Haddjeri-Hopkins Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Pierce Hadley Affiliations University of California San Francisco, School of Medicine, San Francisco, California, United States of America, University of California San Francisco, Institute for Neurodegenerative Diseases, San Francisco, California, United States of America John Haliburton Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America Samantha L. Hao Affiliation: Chan Zuckerberg Biohub, San Francisco, California, United States of America George Hartoularos Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Nadia Herrera Affiliation: University of California San Francisco, School of Medicine, San Francisco, California, United States of America Melissa Hilberg Affiliation: University of California San Francisco, Departm

A very large number of biology and biochemistry laboratory protocols require transferring liquid aliquots from individual containers into individual wells of a multi-well plate, from plates to individual containers, or from one plate to another. Doing this by hand without errors, such as skipping wells, placing two samples in the same well, or swapping sample locations, especially when using plates with 96 wells or more, is difficult and requires enormous operator focus and/or a tedious manual error checking system. We present here a device built to facilitate error-free pipetting of samples from individual barcoded tubes to a multi-well plate or between multi-well plates (both 96 and 384 wells are supported). The device is programmable, modular and easily customizable to accommodate plates with different form-factors, and different protocols. The main components are only a 12.3\" touch screen, a small form-factor PC, and a barcode scanner, combined with custom-made parts can be easily fabricated with a laser cutter and a hobby-grade 3D printer. The total cost is between approximately US $550 and US$ 600, depending on the configuration. Competing Interest Statement The authors have declared no competing interest. Footnotes * Corrected the name of author Andrew T. Cote (was missing middle initial). * https://osf.io/f9nh5/ * https://github.com/czbiohub/WellLit-TubeToWell * https://github.com/czbiohub/WellLit-WelltoWell

Elucidating the wiring diagram of the human cell is a central goal of the post-genomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery, and uncovers that RNA-binding proteins form a specific sub-group defined by unique interaction and localization properties. Furthermore, we discover that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions. Paired with a fully interactive website (opencell.czbiohub.org), we provide a resource for the quantitative cartography of human cellular organization. Competing Interest Statement J.S.W. declares outside interest in Chroma Therapeutics, KSQ Therapeutics, Maze Therapeutics, Amgen, Tessera Therapeutics and 5 AM Ventures. M. M. is an indirect shareholder in EvoSep Biosystems. Footnotes * https://opencell.czbiohub.org/

Tissue culture in 96-well microplates is conventionally a tedious, highly manual process subject to human error and sensitive to individual technique. Here, we describe the Automated Cell Culture Splitter (ACCS), a versatile robotic system for passaging plates of adherent or suspension cells, for routine culture maintenance or more specialized downstream applications such as seeding plates for microscopy. The system is built around the Opentrons OT-2 liquid handling robot and incorporates a novel on-deck imaging-based cell counting instrument which allows it to compensate for density disparities across a source plate and control the number of cells seeded on a per-well basis. We find this solution compares favorably to our existing manual cell culture processes in terms of both accuracy and biological outcomes while cutting the hands-on time required per plate by more than half. The system is designed to be readily adaptable, and we anticipate that it could find uses in a variety of scenarios as an accessible entry point into automation for high-throughput cell culture; to that end, all of the source code and hardware designs are released under open source licenses.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Incomplete institution name corrected to \"Chan Zuckerberg Biohub San Francisco\"* https://github.com/czbiohub-sf/2024-accs-pub

Measuring optical density (OD) is a very common technique in biological laboratories to determine the concentration of a substance in solution or of bacteria (or microscopic particles) in suspension. For example, bacterial cultures engineered to produce (express) a protein or compound of interest are a workhorse of modern molecular biology laboratories. Commonly, the expression of the product is triggered (induced) by a chemical signal added to the culture at the proper time in the growth curve of the culture (typically in the middle of the exponential growth phase, at an OD value of ∼0.6). The most common tool for measuring OD is a spectrophotometer. However, most spectrophotometers are sophisticated, non-portable and expensive laboratory instruments, costing tens of thousands of dollars. Even a very low cost spectrophotometer for educational use costs at least US$1,000. Because of the cost, even well resourced labs have only one instrument, which becomes a bottleneck when multiple bacterial cultures need to be monitored simultaneously. The problem is more acute in developing countries, where multiple labs have to share a single spectrophotometer, or there’s no such instrument at all. Having a cheap and simple device to measure OD would enable multiple people in a laboratory to monitor their bacterial cultures independently, even in resource-limited settings. At the same time, a portable OD meter could be useful for field work. Here we present the detailed build instructions and characterization of a very simple OD meter that costs only US$60, and can measure OD values from ∼0.05 to 2.0.

Recent advances in gene editing are enabling the engineering of cells with an unprecedented level of scale. To capitalize on this opportunity, new methods are needed to accelerate the different steps required to manufacture and handle engineered cells. Here, we describe the development of an integrated software and hardware platform to automate Fluorescence-Activated Cell Sorting (FACS), a central step for the selection of cells displaying desired molecular attributes. Sorting large numbers of samples is laborious, and, to date, no automated system exists to sequentially manage FACS samples, likely owing to the need to tailor sorting conditions (“gating”) to each individual sample. Our platform is built around a commercial instrument and integrates the handling and transfer of samples to and from the instrument, autonomous control of the instrument’s software, and the algorithmic generation of sorting gates, resulting in walkaway functionality. Automation eliminates operator errors, standardizes gating conditions by eliminating operator-to-operator variations, and reduces hands-on labor by 93%. Moreover, our strategy for automating the operation of a commercial instrument control software in the absence of an Application Program Interface (API) exemplifies a universal solution for other instruments that lack an API. Our software and hardware designs are fully open-source and include step-by-step build documentation to contribute to a growing open ecosystem of tools for high-throughput cell biology.

AbstractObjectiveTo investigate potential interactive effects of fine particulate matter (PM2.5) and ozone (O3) on daily mortality at global level.DesignTwo stage time series analysis.Setting372 cities across 19 countries and regions.PopulationDaily counts of deaths from all causes, cardiovascular disease, and respiratory disease.Main outcome measureDaily mortality data during 1994-2020. Stratified analyses by co-pollutant exposures and synergy index (>1 denotes the combined effect of pollutants is greater than individual effects) were applied to explore the interaction between PM2.5 and O3 in association with mortality.ResultsDuring the study period across the 372 cities, 19.3 million deaths were attributable to all causes, 5.3 million to cardiovascular disease, and 1.9 million to respiratory disease. The risk of total mortality for a 10 μg/m3 increment in PM2.5 (lag 0-1 days) ranged from 0.47% (95% confidence interval 0.26% to 0.67%) to 1.25% (1.02% to 1.48%) from the lowest to highest fourths of O3 concentration; and for a 10 μg/m3 increase in O3 ranged from 0.04% (−0.09% to 0.16%) to 0.29% (0.18% to 0.39%) from the lowest to highest fourths of PM2.5 concentration, with significant differences between strata (P for interaction <0.001). A significant synergistic interaction was also identified between PM2.5 and O3 for total mortality, with a synergy index of 1.93 (95% confidence interval 1.47 to 3.34). Subgroup analyses showed that interactions between PM2.5 and O3 on all three mortality endpoints were more prominent in high latitude regions and during cold seasons.ConclusionThe findings of this study suggest a synergistic effect of PM2.5 and O3 on total, cardiovascular, and respiratory mortality, indicating the benefit of coordinated control strategies for both pollutants.