Scientists determine 5,072 essential human genes

In a recent study published in the journal Cell, researchers examined the phenotypic landscape of multiple elusive essential human genes to create a detailed genotype-phenotype resource outlining the phenotypic consequences of disrupting fundamental cellular processes.

Resource: The phenotypic landscape of essential human genes. Image Credit: Billion Photos / ShutterstockResource: The phenotypic landscape of essential human genes. Image Credit: Billion Photos / Shutterstock


Determining the roles of essential genes in distinct cellular processes, including visualizing their contributions to cell morphology, is crucial for understanding the basis for cell growth, proliferation, and functionality.

About the study

In the present study, researchers first performed Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)- CRISPR-associated protein 9 (Cas9)-based functional screening and identified 5,072 fitness-conferring essential genes. Next, they selected four single guide ribonucleic acid (sgRNA) sequences targeting each gene and 250 ‘‘non-targeting’’ sgRNAs, from existing sgRNA libraries.

They delivered the sgRNA library to HeLa cells containing an integrated, doxycycline-inducible Cas9 construct. Next, they fixed the cells and amplified the sgRNA sequences in situ. Then, they stained and imaged cells using four stains that helped them visualize nuclear morphology, deoxyribonucleic acid (DNA) damage response, microtubules, and filamentous actin.

After classifying cells as either interphase or mitotic, the team conducted downstream analyses. This imaging-based screening yielded microscopic images, which helped the researchers extract phenotypic measurements for 1,084 cell image parameters, including measurements of the intensity, subcellular distribution, colocalization of stains, and cellular and nuclear size and shape. The researchers matched sgRNA identities for over 31 million cells with a median of 6,119 cells per gene target for four sgRNAs. Finally, the researchers live-cell pooled imaging to identify genes required for chromosome segregation.

Study findings

The pooled microscopy-based analysis of >31 million individual knockout cells for thousands of fitness-conferring human genes identified specific contributions to core biological processes based on the resulting cellular phenotypes. Cell parameters are directly comparable across a large cell population, referred to as phenotypic ‘‘fingerprints’’ of a gene target. By comparing these phenotype profiles, the researchers defined co-functional gene relationships with sufficient resolution to distinguish related roles in specific cellular processes. However, only four cellular stains were sufficient to identify functional relationships between genes across diverse biological pathways without analyzing specific cellular markers corresponding to each cellular pathway having a distinct function.

Besides identifying established relationships, the current work provided multiple predictions for the contributions of incompletely characterized genes to fundamental cellular processes. For instance, phenotype clustering analysis implicated C7orf26 as a core integrator complex subunit, C1orf131 as a regulator of ribosome biogenesis, and AKIRIN2 in proteasome function. They also identified gene knockouts resulting in defects in mitotic function, including unanticipated roles for the membrane-bound transporters AQP7 and ATP1A1 and cellular osmolarity in promoting accurate chromosome segregation.

In addition, this paper revealed roles for multiple gene expression regulators in controlling cell division, including the predicted transcription factor ZNF335, the DREAM complex (LIN52), the 30-end mRNA processing complex (CLP1), and the minor spliceosome (RNPC3) in the expression of specific cell division components. These examples highlight the power of optical screening to identify co-functional genes across diverse biological pathways, with the potential for further discovery from the data published here.


The researchers efficiently leveraged complex, multidimensional image-based phenotypes to yield functionally relevant gene clusters across diverse cellular processes at a scale of much greater magnitude than any individual image-based pooled profiling screen. As a result, researchers now have a powerful data resource to explore and a comprehensive testbed to develop analytical techniques.

Two proteins might act in a single biological pathway without displaying a direct interaction. Besides precisely identifying co-functional genes across a wide range of cellular processes, the current study provided a highly scalable orthogonal approach to proteome-wide protein interaction studies. Furthermore, the quantitative image-based phenotypic profiling remarkably defined gene clusters for pan-essential genes, such as ribosome components, which do not show varying requirements across cell lines. In many cases, this analysis also identified additional genes and a more fine-grained resolution for their gene clusters, which allowed distinguishing between core ribosome subunits and ribosome-biogenesis factors.

The study approach was also highly successful at identifying gene clusters for morphological processes, including cytokinesis, nuclear transport, chromosome condensation, and others, which might be otherwise difficult to identify based on transcriptional changes. It also produced complex profile data, and perturbation identity for each cell enabled correlating multiple high-dimensional phenotypes directly with individual perturbations in a single experiment. Overall, relatively inexpensive, image-based pooled CRISPR profiling screens emerged as a robust strategy for defining gene networks and the functional roles of human genes. Moreover, pooled image-based screens were comparatively simple to scale compared to arrayed image-based screens, with intermixed controls providing a robust statistical basis for comparisons.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.


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