Kuchina Lab Overview

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“Variety is the spice of life: Survival of a bacterial population may depend on a few rare cells programmed to behave differently. To develop effective therapies, we must understand how the behavior of a microbial community emerges from the diverse contributions of its individual members.”

–Anna Kuchina, PhD, ISB Assistant Professor
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Research Overview

Even genetically identical bacterial cells often express different sets of genes and behave differently. Not unlike individual cells in the human body, they may assume specialized roles for the benefit of the whole community. We are developing and applying high-throughput single-cell genomic technologies to identify and characterize diverse functional subpopulations of bacteria within complex microbial samples. We are interested in unraveling the roles and behavior of individual bacteria in challenging environments such as the human host, and within structured multi-species communities known as biofilms.

Research Foci

Single-cell studies reveal heterogeneity of bacterial gene expression even in genetically identical bacteria grown under the same lab conditions. Bacteria differentiate into distinct subpopulations responsible for different functional activities to benefit the community. For example, gene expression programs governing developmental and stress-response states such as competence may switch on stochastically in a small number of single cells. Additionally, very rarely bacterial cells may enter a dormant state known as persistence which permits survival under antibiotic treatment and leads to the development of antibiotic resistance of bacterial infections. Population level gene expression measurements are insufficient to resolve such states which have been only discovered through single-cell methods.

Given the complexity observed even in lab-grown monocultures, this heterogeneity is likely further magnified within complex environments and in the presence of other species, where bacteria employ bet-hedging and labor division strategies to enhance their fitness. We develop and apply single-cell genomic tools for unbiased, high-throughput expression profiling of bacterial consortia such as the animal-associated microbiota and single- and polymicrobial biofilms. We employ custom technology based on combinatorial barcoding which is scalable, versatile, and customizable for use in various settings including the host-pathogen interface. Altogether, we aim to obtain a much higher resolution view into the lifestyles and behavior of bacterial consortia that traditionally resisted single-cell investigation.

Single-cell gene expression landscape in a biofilm
Biofilms are spatially structured bacterial communities that contribute to chronic bacterial diseases. Many cells within the biofilm are shielded from host defenses and drugs. In addition, multiple species frequently co-occur and interact in a polymicrobial biofilm. What specialized subpopulations emerge in biofilms in response to the environmental stressors such as antimicrobial agents? How are these responses shaped in the presence of interacting or competing species? We aim to create single-cell gene expression maps of model single- and multiple-species biofilms and their responses to external stimuli by a combination of single-cell RNA sequencing and single-cell time-lapse imaging.

Bacterial and host phenotypic variation during intracellular infection
When pathogenic bacteria invade host cells and tissues, the interactions between individual host cells and the bacteria are typically highly variable. The cellular outcome of the infection may range from complete clearance of infection to persistent survival of intracellular bacteria. We aim to predict the outcomes of intracellular infection at the level of individual interacting cells based on the simultaneous measurements of both host and pathogen gene expression states. To this end, we develop and employ custom dual host-pathogen single-cell sequencing technologies.

Single-cell biology of the microbiome
Microbiota, complex consortia of bacteria, are found in all environments, including the human body. The gut microbiome is currently understood to represent a complex “organ” in the body affecting a multitude of body systems and implicated in a wide range of diseases. The community-wide gene expression profiles obtained through metatranscriptomics allow to establish the activity of various genetic pathways within the microbiome. However, bulk metatranscriptomics cannot determine which genes are co-expressed within the same cell. We aim to obtain functional single-cell atlases of both natural and engineered microbiomes in health and disease using targeted genomic and multiomic technologies based on combinatorial barcoding. By gaining the knowledge of which genes are coexpressed in the same subset of bacterial cells, we will become able to identify and isolate strains or subpopulations producing specific metabolites or pathogenic factors. This knowledge will enable effective therapies that manipulate the microbiome in a targeted fashion, resulting in development of safe novel therapeutics.