Functional Genomics Platform



CRISPRi(nterference) and CRISPRa(ctivation)

The application of the bacterial CRISPR/Cas9 system in mammalian cells has revolutionized the manipulation of the mammalian genome. Cas9 is a nuclease that can form a complex with an RNA, termed small guide or sgRNA, which directs Cas9 to a cleave specific DNA sequences. Very recently, we have co-developed a screening platform using a catalytically dead version of Cas9 (dCas9) to recruit either a transcriptional repressor domain (CRISPRi) or an array of transcriptional activator domains (CRISPRa) to specific DNA loci close to the transcription start site (TSS) of mammalian genes. We determined rules for sgRNA design and targeting that enable robust, reversible repression or activation of gene transcription. Based on these rules, we constructed genome-wide sgRNA libraries optimized for either CRISPRi or CRISPRa pooled genetic screens based on the same quantitative framework as our previous RNAi screens. CRISPRi-based loss-of-function screens and CRISPRa-based gain-of-function screens yield rich, complementary insights into cellular pathways.

Gilbert LA, Horlbeck MA, Adamson B, Villalta JE, Chen Y, Whitehead EH, Guimaraes C, Panning B, Ploegh HL, Bassik MC, Qi LS, Kampmann M*, Weissman JS* (2014). Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159(3): 647-661. NIHMS630425 *Corresponding authors.

Systematic genetic interaction maps

Kampmann Lab Functional Genomics Platform

Overview: Our functional genomics platform


We spearheaded the development of a technology platform that enables the elucidation of pathways in mammalian cells. Our platform integrates three experimental stages. First, we identify a set of genes of interest in a genome-wide primary screen (A). Our original implementation was based on RNA interference, and we developed ultracomplex shRNA libraries and a quantitative framework to overcome problems with off-target effects that have plagued RNAi screen in the past. Next, we individually clone shRNAs targeting hit genes from the primary screen to generate a focused shRNA library, which can be used to compare different cell lines or phenotypic assays (B). Finally, we construct a double-shRNA library targeting all pairwise combinations of hit genes, which enables the massively parallel measurement of 100,000s of combinatorial phenotypes in a pooled screen, from which we generate a high-density genetic interaction map (C).

Genetic Interactions

Definition of genetic interactions

Genetic interactions (GIs) quantify the phenotypic effect that loss of function of one gene has on the loss of function of a second gene, and can be classified as buffering (double mutant less severe than expected, classically for genes in the same pathway) or synergistic/synthetic-lethal (double mutant more severe than expected, classically for genes acting in parallel pathways). GIs are used in classical genetics to deduce pathway relationships between individual genes.

Genetic Interaction Map

Genetic interaction map


In high-density GI maps, the systematic measurement of all GIs between one gene and a large number of other genes provides an unbiased “fingerprint” of the gene’s cellular function, and functionally related genes have similar GI patterns. Therefore, correlation of GI patterns reveal functional pathways. GI maps were successfully used in microorganisms, but the technology we developed now enables their use in mammalian cells. In a pilot study, we generated a GI map for genes controlling the susceptibility of human cells to the toxin ricin. This GI map recapitulated known biology (clusters corresponding to physical complexes, or functional interaction partners such as ARF1 and its nucleotide exchange factor GBF1), and also yielded new insights, such as the existence of two functionally distinct mammalian TRAPP complexes, which we could validate biochemically.

GImap, the computational pipeline we developed for the analysis of pooled screens and the construction of genetic interaction maps is freely available here.

Kampmann M, Bassik MC and Weissman JS (2013) Integrated platform for genome-wide screening and construction of high-density genetic interaction maps in mammalian cells. Proceedings of the National Academy of Sciences 110(25): E2317-26. PMC3690882.

Bassik MC*, Kampmann M*, Lebbink RJ, Wang S, Hein MY, Poser I, Weibezahn J, Horlbeck MA, Mann M, Hyman AA, LeProust EM, McManus MT and Weissman JS (2013) A Systematic Mammalian Genetic Interaction Map Reveals Pathways Underlying Ricin Susceptibility. Cell, 152:909-22. PMC3652613. *Equal contribution and corresponding authors.

Gene-drug interactions

One important application of our screening technology is the identification of genes that control the sensitivity of cells to a given drug. Results from such screens have several implications for the development of therapies:

  • Identification of the relevant cellular targets of a new compounds - often a huge challenge for compounds identified in phenotypic screens.
  • Identification of possible resistance mechanisms to anti-cancer drugs. Drug resistance in cancer cells severely limits the effectiveness of chemotherapy and targeted therapies.
  • Identification of synergistic (synthetic lethal) therapeutic targets in cells, which can guide the rational development of combination therapies.


Acosta-Alvear D, Cho MY, Wild T, Buchholz TJ, Lerner AG, Simakova O, Hahn J, Korde N, Landgren O, Maric I, Choudhary C, Walter P, Weissman JS, Kampmann M (2015) Paradoxical resistance of multiple myeloma to proteasome inhibitors by decreased levels of 19S proteasomal subunits. eLife 10.7554/eLife.08153

Sidrauski C, Tsai JC, Kampmann M, Hearn BR, Vedantham P, Jaishankar P, Sokabe M, Mendez AS, Newton BW, Tang EL, Verschueren E, Johnson JR, Krogan NJ, Fraser CS, Weissman JS, Renslo AR, Walter P. (2015) Pharmacological dimerization and activation of the exchange factor eIF2B antagonizes the integrated stress response. Elife. 2015 Apr 15;4. doi: 10.7554/eLife.07314. PMID 25875391.

Julien O, Kampmann M, Bassik MC, Zorn JA, Venditto V, Shimbo K, Agard NJ, Shimada K, Rheingold AL, Stockwell BR, Weissman JS, Wells JA (2014) Unraveling the mechanism of cell death induced by chemical fibrils. Nature Chemical Biology 10: 969-976. PMID 25262416.

Matheny CJ, Wei MC, Bassik MC, Donnelly AJ, Kampmann M, Iwasaki M, Piloto O, Solow-Cordero DE, Bouley D, Rau R, Brown P, McManus MT, Weissman JS and Cleary ML (2013) Next-generation NAMPT inhibitors identified by sequential high-throughput phenotypic chemical and functional genomic screens. Chemistry & Biology. 20:1352-1363. PMID 24183972.