Scientists report that a novel technique that analyzes how individual immune cells react to the bacteria that cause tuberculosis (TB) could pave the way for new vaccine strategies against this deadly disease, and provide insights into fighting other infectious diseases around the world.
The approach was developed in the lab of David Russell, PhD, the William Kaplan Professor of Infection Biology in Cornell’s department of microbiology and immunology in the College of Veterinary Medicine, and detailed in a new collaborative research paper (“Single cell analysis of M. tuberculosis phenotype and macrophage lineages in the infected lung”) published in the Journal of Experimental Medicine.
“In this study, we detail a novel approach that combines bacterial fitness fluorescent reporter strains with scRNA-seq to simultaneously acquire the host transcriptome, surface marker expression, and bacterial phenotype for each infected cell. This approach facilitates the dissection of the functional heterogeneity of M. tuberculosis–infected alveolar (AMs) and interstitial macrophages (IMs) in vivo,” write the investigators.
“We identify clusters of pro-inflammatory AMs associated with stressed bacteria, in addition to three different populations of IMs with heterogeneous bacterial phenotypes. Finally, we show that the main macrophage populations in the lung are epigenetically constrained in their response to infection, while inter-species comparison reveals that most AMs subsets are conserved between mice and humans.
“This conceptual approach is readily transferable to other infectious disease agents with the potential for an increased understanding of the roles that different host cell populations play during the course of an infection.”
For years, Russell’s lab has tried to decipher how Mycobacterium tuberculosis (Mtb), the bacteria that cause tuberculosis, infect. and persist in their host cells, which are typically immune cells called macrophages.
Combining two analytical tools
Now the team has combined two analytical tools that each target a different side of the pathogen-host relationship: “reporter” Mtb bacteria that glow different colors depending on how stressed they are in their environment; and single-cell RNA sequencing (scRNA-seq), which yields RNA transcripts of individual host macrophage cells.
“For the first time ever, Dr. Davide Pisu in my lab combined these two approaches to analyze Mtb-infected immune cells from an in vivo infection,” Russell said.
After infecting mice with the fluorescent reporter Mtb bacteria, Russell’s team was able to gather and flow-sort individual Mtb-infected macrophages from the mouse lung. The researchers then determined which macrophages promoted Mtb growth (red-glowing bacteria) or contained stressed Mtb unlikely to grow (green-glowing bacteria).
Next, they took the two sorted, infected macrophage populations and ran them through single-cell RNA sequencing analysis, thereby generating transcriptional profiles of each individual host cell in both populations.
When the scientists compared the macrophage single cell sequencing data with the reporter bacteria phenotype, they found an almost perfect one-to-one correlation between the fitness status of the bacterium and the transcriptional profile in the host cell. Macrophages that housed green bacteria also expressed genes that were known to discourage bacterial growth, while those with red bacteria expressed genes known to promote bacterial growth.
“What absolutely stunned us is how well it worked,” Russell said. “When Davide Pisu showed me the analysis I nearly fell off my chair.”
Normally, phenotypes and transcriptional profiles are two characteristics that seldom come together in a perfect match, and this almost never happens from in vivo data.
Near-perfect matchup
This near-perfect matchup revealed new nuances, according to Russell.
“While our previous results identifying the resident alveolar macrophages (AM) as permissive and the blood monocyte-derived recruited macrophages (IM) as controlling Mtb infection was correct in a broad sense, we found, unsurprisingly, that this was an oversimplification,” Russell pointed out.
There was variation even within these two different macrophage types: Some AM cells controlled Mtb growth while some IMs were allowing bacterial expansion. The team found that comparable subsets of immune cells were present in both human and mouse lung samples.
An additional step in the study was to look at whether the responses of AM and IM cells to Mtb were epigenetically controlled, i.e., the cells’ traits are due to certain genes being turned off or on by molecular switches This could explain how two sets of macrophages respond differently. Using a read-out of a cell’s epigenetic landscape, they found that this was the case.
“The analysis showed that when these cells are exposed to Mtb or the vaccine strain- through infection or vaccination, respectively, their epigenetic programming has a major influence over whether their response leads to disease control or progression,” explained Russell.
The Russell lab next plans to identify novel therapeutics.
“We’re going to begin by screening libraries of known epigenetic inhibitor compounds to see which ones might be useful in modifying the immune response,” explained Russell.
If they do find promising compounds—ones that push macrophages towards an anti-Mtb behavior–they could potentially be used in combination with vaccines to assist a patient’s immune system in protecting against tuberculosis.
The finding lays a foundation for more powerful studies on how pathogens affect individual cells, allowing for a holistic examination of the system.
“This is a roadmap that lets us look across an entire population of cells and see how a single perturbation impacts the cells across that population,” Russell said. “We can test for drug efficacy in in vivo infection without any preconceived limitation on how compounds may function.”
This approach is extremely flexible and could be used in the study of any intracellular pathogen, including viruses, and is readily applicable to any animal challenge model, he added.
