M20 Genomics

M20 Genomics Presents MscRNA-seq: Pioneering the World’s First Platform for Single-Cell Transcriptome Sequencing in Bacteria

发布时间:2024-01  /  浏览次数:195 次

In March 2022, M20 Genomics proudly launched MscRNA-seq (Microbial-single-cell RNA sequencing). This pioneering platform marks a significant leap as single-cell transcriptome sequencing technology enters the commercial stage within the field of microbial research. The availability of MscRNA-seq facilitates the transcendence of current technological constraints, enabling the comprehensive study of bacterial drug resistance and persistence mechanisms, pathogenicity, and intricate bacterial-host interactions.


The Current State of Single-Cell Sequencing Technology

Single-cell sequencing technology has emerged as a powerful tool for detecting gene expression and other information at the level of individual cells. This innovation addresses a critical limitation in traditional research methodologies, where sequencing bulked samples from tissues or cell populations can mask the transcriptional heterogeneity between individual cells by averaging across a population.

Antimicrobial resistance (AMR) is emerging as one of the major public health problems of this century. In its 2014 Global Antimicrobial Resistance Report, the World Health Organization (WHO) disclosed that drug-resistant bacterial infections claimed over 700,000 lives globally within a single year. Predictive models suggest a worrisome scenario, projecting that by 2050, fatalities from antibiotic resistant bacterial infections could exceed 10 million per year, surpassing even the projected 8.2 million cancer-related deaths for the same period.

The heterogeneity in antibiotic resistance displayed among individual bacterial cells poses a challenge to traditional high-throughput sequencing methods. Implementing single-cell sequencing for bacterial cells unveils new opportunities to delve deeply into the complex mechanisms underlying antibiotic resistance.

Figure: Projected global deaths from infection with super-resistant bacteria in 2050 (Review on Antimicrobial Resistance. Antimicrobial Resistance: Tackling a Crisis for the Health and Wealth of Nations. 2014)


The Evolving Landscape of Single-Bacterium RNA Sequencing Technology

Considering recent studies leveraging single-bacterium genome technology, its remarkable resolution for unculturable microorganisms has become evident. This sequencing methodology enables researchers to analyse over 50,000 bacteria simultaneously. Such capabilities pave the way for groundbreaking discoveries concerning the distribution of antibiotic resistance genes, virulence determinants, and other characteristics of microbial communities[1]. Moreover, the single-bacterium genome technology can be used for the classification and functional annotation, aiding in the discovery of new branches and species, and assisting the exploration of differences between bacterial subspecies.

While single-bacterium genome sequencing has been realized successfully, multiple aspects pose challenges obtaining bacterial transcriptomes at the single-cell level. These include their small cellular dimensions, the complexity of partitioning individual cells, resilient cell walls requiring rigorous lysing conditions and the low quantity and stability of mRNA. Most importantly, the absence of poly(A) tails on bacterial transcripts hinders the direct application of established single-cell RNA sequencing (scRNA-seq) methods, which have been designed for eukaryotes and rely on capturing poly(A) tails.

In the rapidly advancing field of single-cell sequencing, various methods have been developed to facilitate the analysis of bacterial transcriptomes. PETRI-seq [2] and microSPLiT [3] utilize the split-pool technique to label individual bacterial cells. In contrast, BacDrop [4] employs two rounds of combinatorial barcoding and microfluidics. While this approach effectively overcomes throughput limitations and associated challenges in realizing commercial automation applications linked to split-pool techniques, there remains significant potential for improving detection sensitivity in all mentioned methods.


M20 Genomics' MscRNA-seq Platform: Emerging as a Pioneer

With M20's innovative MscRNA-seq platform, we've achieved high-throughput single-bacterium RNA sequencing, delivering superior detection sensitivity and reliable reproducibility.

Figure: VITAcruizer Single-cell Partitioning System of MscRNA-seq platform

Utilizing our advanced MscRNA-seq platform, we extensively examined a diverse spectrum of bacteria, including Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, and more. Within the majority of samples, we have successfully detected a median count of 150-200 genes within a single bacterial cell, with some samples reaching a median gene count per bacterial cell of 1,000 and beyond.

Figure: Distribution of detected genes obtained from single-bacterium

Our cutting-edge technology not only facilitates the characterization of transcriptomes in well-established model organisms but also empowers in-depth study on bacterial species that are challenging to cultivate in laboratory settings. M20 Genomics' MscRNA-seq platform represents a remarkable step beyond current technological constraints and holds promise for a deeper understanding of microbiology. It signifies a transformative approach, introducing a technological evolution. From deciphering antibiotic resistance to unravelling intricate bacteria-host interactions, its impact spans across research and potential clinical applications.


[1]Lan, Freeman, et al. "Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding." Nature biotechnology 35.7 (2017): 640-646.

[2] Blattman, Sydney B., et al. "Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing." Nature microbiology 5.10 (2020): 1192-1201.

[3] Kuchina, Anna, et al. "Microbial single-cell RNA sequencing by split-pool barcoding." Science 371.6531 (2021): eaba5257.

[4] Ma, Peijun, et al. "Bacterial droplet-based single-cell RNA-seq reveals antibiotic-associated heterogeneous cellular states." Cell 186.4 (2023): 877-891.

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