Everybody knows that the immune system is hugely complex.
#singlecell sequencing has (arguably) done more for the immune system than for other health applications.
Via #scRNAseq, we discovered & characterized crazily detailed immune cell phenotypes.
Such detailed phenotypes have been found in both healthy and diseased tissues.
I wrote several threads about this topic and find it to be one of the most foundational & fascinating progresses that have happened in biomedicine in the past 10 years.
Still, much more single cell data has been generated in mice 🐁 than in humans.
Therefore, age-related immune changes are better characterized in the mouse #singlecell ecosystem.
This table summarizes findings from multiple publications on mouse data (same reference as above).
By analyzing the two tables, we see that many immune age-related changes are shared among mice & humans.
Still, some are different.
Humans are constantly exposed to infections (unlike🐁), which may explain the more complex human age-related changes(e.g. in CD8 Tcells or Bcells)
Next, let's dive deeper into some of these immune age-related & age-induced changes, and also discuss the data resources based on which these conclusions were derived.
1. Inflammation:
Unresolved systemic inflammation in the absence of active pathogen stimulation is one of the main features of immune aging.
This is also called inflammageing, and is characterized by an unjustified increased production of pro-inflammatory factors.
A good paper to understand inflammation-specific age-related immune changes (such as via new gene signatures) is this 2020 @Nature article, which aggregates & analyzes data from multiple ages across the mouse lifespan.
As the activity of the thymus (where T cells are produced) decreases gradually with age, so does the number of T cells.
In contrast, myeloid cells expand, and myeloid-biased hematopoietic stem cells (HSCs) accumulate in hematopoietic niches with age.
This 2019 @Nature paper characterizes the #singlecell transcriptional landscape of mouse bone marrow vascular, perivascular and osteoblast populations, both at homeostasis and under conditions of stress-induced hematopoiesis.
T cells are one of the most important immune system players, defending us from pathogens & tumors.
They are also one of the populations suffering most from #aging: loss in naive CD8+, increase in memory Tcells & increase in clonality (i.e. decreased diversity).
Next, this 2020 @ImmunityCP paper comprehensively characterizes immune aging across four different mouse tissues (spleen, peritoneum, lungs and liver) by both #scRNAseq and #scTCRseq.
It finds a specific age-associated T cell subpopulation (PD1+TOX+CD8+), which accumulates with age in all the tissues evaluated, making up to 60% of all CD8+ T cells in these tissues.
This is interesting because this subpopulation seems to be systemic & not tissue-specific.
‼️ Next, a core reference paper for any #singlecell data analyst's portfolio of #DataScience resources:
Tabula Muris Sensis 2020 @Nature paper: a #scRNAseq atlas across the mouse lifespan that includes data from 23 tissues and organs.
Fibroblasts are key players in multiple diseases, in particular cancer. A recent #scRNAseq paper characterizes in detail the stroma landscape across cancer types, noting the complex interaction among stroma & immune cells in driving tumor progression
In general, aged stroma and endothelial cells become enriched in pathways related to cytokine signaling and inflammation.
Such changes are potentially related to the increased prevalence of down-regulation & dys-regulation of immune responses in advanced age.
6. Senescent cells
Senescence is the last stage of cell differentiation and means permanent withdrawal from the cell cycle. It often happens to damaged or stressed cells.
Senescent cells secrete chemokines, cytokines & growth factors, leading to low-grade systemic inflammation.
Senescent cells are usually recognized & eliminated by immune cells.
New: the monthly roller-coaster through October’s coolest life science papers is here 🚀🧬
3-sentence summaries of papers on evolution, single cell methodologies, genetic screens & more.
And, only for October, an educational video on fighting cancer🤺 as a bonus.
Enjoy 3x10!
1. Assembly theory (Sharma et al., Nature)
The most (in)famous paper I read this month proposes a new framework (assembly theory, that is) to explain basically everything… or, more specifically, “to unify descriptions of evolutionary selection across physics and biology” 1/3
This paper is not an easy read for anybody (in particular evolutionary biologists), but, to its merit, it sparked scientific discussions by being different than what is expected for a scientific paper describing evolution. 2/3
The human genome is gradually unravelling its secrets 🎁
AlphaMissense model @ScienceMagazine: one more path lit up by deep learning in exploring the code of life 🧬
We now know with high confidence if 89% of ALL missense variants are benign or pathogenic
Key contributions🧵🧵
First things first:
Missense variants = genetic variants (i.e DNA bases) that change the amino acid sequence (i.e groups of 3 bases, building blocks of proteins) in proteins.
Missense variants are more important than non-missense ones, as more likely to have functional impact.
Now, even if a variant changes the amino acid structure of a protein (i.e it is missense), it is not necessarily that the variant also impacts the function of its corresponding protein.
Further, even if protein function gets impacted, it isn't clear in which way or by how much.
- 500,000 cells with MIBI of 37 antibody panel
- 66 individuals (6-20 weeks gestation)
Immune tolerance model proposed for how the structure & function of the maternal endometrium transforms to promote the regulated invasion of genetically dissimilar fetal cells
Cancer is a terrible disease, and also one that we all know too well.
It is not a new problem, rather one that exists since thousands of years & is studied in unimaginable detail.
Then why do people still die of cancer?
Let's start understanding this by taking a step back.
It’s 1938, and Public Health Services are advising people that detecting and treating cancers early will save their lives.
Now fast-forward nowadays. We hear the exact same core message from the Public Health Services of our times, gradually and consistently backed up by more and more data.