Skip to main content

MAC-Seq: Coupling Low-Cost, High-Throughput RNA-Seq with Image-Based Phenotypic Screening in 2D and 3D Cell Models

Part of the Methods in Molecular Biology book series (MIMB,volume 2691)


Transcriptomic profiling has fundamentally influenced our understanding of cancer pathophysiology and response to therapeutic intervention and has become a relatively routine approach. However, standard protocols are usually low-throughput, single-plex assays and costs are still quite prohibitive. With the evolving complexity of in vitro cell model systems, there is a need for resource-efficient high-throughput approaches that can support detailed time-course analytics, accommodate limited sample availability, and provide the capacity to correlate phenotype to genotype at scale. MAC-seq (multiplexed analysis of cells) is a low-cost, ultrahigh-throughput RNA-seq workflow in plate format to measure cell perturbations and is compatible with high-throughput imaging. Here we describe the steps to perform MAC-seq in 384-well format and apply it to 2D and 3D cell cultures. On average, our experimental conditions identified over ten thousand expressed genes per well when sequenced to a depth of one million reads. We discuss technical aspects, make suggestions on experimental design, and document critical operational procedures. Our protocol highlights the potential to couple MAC-seq with high-throughput screening applications including cell phenotyping using high-content cell imaging.

Key words

  • RNA-seq
  • Multiplexing
  • High-throughput screening
  • 3D cell models
  • High-content imaging

Equal author contribution: Xiang Mark Li and David Yoannidis

Equal senior author contribution: Gisela Mir Arnau, Timothy Semple and Kaylene J. Simpson

This is a preview of subscription content, access via your institution.

Buying options

USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more


  1. Ye C, Ho DJ, Neri M et al (2018) DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery. Nat Commun 9:4307

    CrossRef  PubMed  PubMed Central  Google Scholar 

  2. Li J, Ho DJ, Henault M et al (2021) DRUG-seq provides unbiased biological activity readouts for drug discovery. Biorxiv 2021(06):07.447456

    Google Scholar 

  3. Alpern D, Gardeux V, Russeil J et al (2019) BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing. Genome Biol 20:71

    CrossRef  PubMed  PubMed Central  Google Scholar 

  4. Moyerbrailean GA, Davis GO, Harvey CT et al (2015) A high-throughput RNA-seq approach to profile transcriptional responses. Sci Rep 5:14976

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  5. Todorovski I, Feran B, Fan Z et al (2022) RNA decay defines the therapeutic response to transcriptional perturbation in cancer. Biorxiv 2022(04):06.487057

    Google Scholar 

  6. Kong IY, Trezise S, Light A et al (2022) Epigenetic modulators of B cell fate identified through coupled phenotype-transcriptome analysis. Cell Death Differ 29:2519–2530

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  7. So J, Lewis AC, Smith LK et al (2022) Inhibition of pyrimidine biosynthesis targets protein translation in acute myeloid leukemia. EMBO Mol Med 14:e15203

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wang Y, Jeon H (2022) 3D cell cultures toward quantitative high-throughput drug screening. Trends Pharmacol Sci 43:569–581

    CrossRef  PubMed  Google Scholar 

  9. Szymański P, Markowicz M, Mikiciuk-Olasik E (2011) Adaptation of high-throughput screening in drug discovery—toxicological screening tests. Int J Mol Sci 13:427–452

    CrossRef  PubMed  PubMed Central  Google Scholar 

  10. Hughes RE, Elliott RJR, Dawson JC et al (2021) High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need. Cell Chem Biol 28:338–355

    CrossRef  CAS  PubMed  Google Scholar 

  11. Choo N, Ramm S, Luu J et al (2021) High-throughput imaging assay for drug screening of 3D prostate cancer organoids. Slas Discov 26:1107–1124

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ramm S, Vary R, Gulati T et al (2022) High-throughput live and fixed cell imaging method to screen Matrigel-embedded organoids. Organoids 2:1–19

    CrossRef  Google Scholar 

Download references


We thank members of the Molecular Genomics Core facility and Victorian Centre for Functional Genomics for their valuable input into the development of these methodologies. The Victorian Centre for Functional Genomics (KJS) is funded by the Australian Cancer Research Foundation (ACRF), Phenomics Australia, through funding from the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS) program and the Peter MacCallum Cancer Centre Foundation. This project has been supported by specific project funding through Phenomics Australia (KJS) and separate Peter MacCallum Foundation grants to GMA and SR.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Xiang Mark Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Li, X.M. et al. (2023). MAC-Seq: Coupling Low-Cost, High-Throughput RNA-Seq with Image-Based Phenotypic Screening in 2D and 3D Cell Models. In: Jenkins, B.J. (eds) Inflammation and Cancer. Methods in Molecular Biology, vol 2691. Humana, New York, NY.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3330-4

  • Online ISBN: 978-1-0716-3331-1

  • eBook Packages: Springer Protocols