Title:
“A Microfluidic Platform for Quantitative Analysis of Single Mycobacteria
Cells”

Author:
Jason P. Keller

 

Abstract:

Mycobacterium tuberculosis (MTB), the causative agent of tuberculosis (TB), is
the leading bacterial cause of death worldwide. A significant barrier to global
MTB eradication is ‘latent’ TB infection, where MTB persists in the human host
in a metabolically dormant and highly drug-tolerant state. Latently infected
individuals constitute a vast global reservoir of disease (~2 billion people
worldwide), and the heightened drug tolerance of dormant MTB necessitates long
antibiotic treatments (up to 9 months of combination antibiotic therapy).
MTB dormancy is thought to be the result of an adaptive response to host-induced
stresses, involving coordinated transcriptional regulation of hundreds of genes
as well as numerous metabolic changes. Currently, our understanding of this
process is limited by a lack of tools for studying dynamic behavior in single
cells. Gene regulation is a dynamic phenomenon that occurs within each cell
individually, but many assays rely on steady-state measurements of a population
average and thus fail to capture important information about the dynamics of
cellular behavior. Additionally, cell-to-cell phenotypic variation has been
identified as a key source of microbial drug tolerance, further highlighting the
need for single-cell studies.


To address this need, we developed a microfluidic platform to study Mycobacteria
species at the single-cell level. This platform enables on-chip culture and
fluorescent imaging of live cells in precisely controlled conditions, and can
thus be used to study dynamic processes within single cells as well as
phenotypic heterogeneity across a cellular population.  We used this platform to
obtain diverse new insights about mycobacterial biology, using the fast-growing
mycobacterium M. smegmatis.  1) We directly observed gene regulation by the
transcription factor KstR in single cells, confirming regulatory interactions
that had been predicted computationally.  2) We analyzed morphology, growth, and
division data across hundreds of single cells and found that cell division in
Mycobacteria is governed using size-based, rather than time-based, control
mechanisms.  3) We found that individual cells exhibit considerable differences
in their responses to antibiotic stress, and that these differences have
implications for cellular survival.