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Opportunities and Challenges of the Connectivity Map and L1000 Gene Expression Profiling

High-throughput gene profiling and drug screening projects have generated vast amounts of biological data, giving researchers endless opportunities to untangle complicated biological questions.

The Connectivity Map, driven by the L1000 gene expression profiling technology, is a leader in these screening efforts. It aims to provide a comprehensive resource for studying the effects of thousands of compounds on cellular functions and diseases (Subramanian et al., 2017).

But it is crucial for researchers to understand both the opportunities and challenges with the Connectivity Map and the L1000 technology before using these resources to contribute to drug discovery projects.

Here we discuss these opportunities and challenges alongside a novel gene expression profiling option called MERCURIUS™ DRUG-seq that might help address some of the key pitfalls with the Connectivity Map and L1000 technology.


A large-scale drug discovery and development resource

The foremost opportunity for researchers and drug discovery projects using the Connectivity Map is the discovery of hidden connections between genes, diseases, and potential therapeutics. This is thanks to over 1.3 million L1000 gene expression profiles that measure how different drugs and genetic perturbations affect the activity of genes in human cells (Subramanian et al., 2017).

These publicly available profiles act as a look-up table for researchers to instantly compare the differentially expressed genes from their own drug or gene knockdown/overexpression treatments with profiles in the Connectivity Map.

This helps to rapidly uncover hidden biological pathways and functions of genes alongside novel molecular targets and mechanisms of action of drugs. It can also help prioritize candidates for further testing and validation while providing insights into the molecular basis of drug response and resistance.

But such a large-scale gene expression compendium was only made possible by the L1000 technology.

With L1000, scalability and cost-effectiveness are dramatically improved compared to standard RNA-seq methods, as only around 1000 genes are directly measured, with an additional 11,000 computationally inferred.

Despite these benefits, the L1000 technology has some challenges that may hinder biotechnology companies' drug discovery and development.


L1000: What is missed?

One major challenge with the Connectivity Map is that the L1000 technology directly measures less than 5% of protein-coding genes. This limits the ability of researchers to correctly identify relevant target candidates, off-target effects, or novel therapeutic mechanisms and to integrate the L1000 data with other types of transcriptomic data, such as RNA-seq (Jeon et al., 2022).

Similarly, if a different set of directly measured genes was used, it might alter the results when inferring the gene expression profiles for the additional 50% of the protein-coding transcriptome.

To address these issues, researchers have now developed innovative deep-learning computational methods to generate RNA-seq-like profiles for over 23,000 genes, but this still relies on less-than-perfect inference instead of direct measurements of mRNA expression (Jeon et al., 2022).

Instead of relying on these computational methods, using RNA-seq-based approaches would measure exact amounts of mRNA transcriptome-wide, providing broader, more accurate gene expression profiles. However, both cost and scalability previously presented significant hurdles for large-scale projects.

Thankfully, the cost of bulk RNA-seq has dropped dramatically in recent years thanks to advances in sequencing technology combined with extensive sample multiplexing made possible by sample barcoding and sequencing of the 3’ end of mRNA molecules (Alpern et al., 2019).


How to overcome these challenges

For biotechnology or pharmaceutical companies interested in drug discovery or development, these new 3’ mRNA-seq methods, such as MERCURIUS™ DRUG-seq, combine the scalability and cost-effectiveness of L1000 technology with the advantage of direct transcriptome-wide measurements of over 20,000 genes per sample.

Combined with the millions of L1000 gene expression profiles freely available from the Connectivity Map, this novel bulk 3’ mRNA-seq technology represents a powerful approach for detecting and validating novel modes of action and potential side effects and realizing the biological relevance of promising therapeutics.

Please contact us to learn more about MERCURIUS™ DRUG-seq, the Connectivity Map, and the L1000 technology.



  • Alpern, D. et al. (2019) ‘BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing’. Genome biology, 20(1), pp.1-15. Available at:

  • Jeon, M. et al. (2022) ‘Transforming L1000 profiles to RNA-seq-like profiles with deep learning’, BMC bioinformatics, 23(1), p.374. Available at:

  • Subramanian, A. et al. (2017) ‘A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles’, Cell, 171(6), pp. 1437-1452.e17. Available at: