Simona Cristea Profile picture
Dec 21, 2022 28 tweets 14 min read Read on X
Our immune system is essential in keeping us healthy.

But the immune system also changes profoundly as we age.

Why is that? Could we prevent it?

Let's see how #singlecell biology can help us better understand #immune #aging

🧵👇
First, some background.

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. Image
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.
Many such small, but important, discoveries are still under-appreciated.

They are initially derived from animal experiments & observational clinical data.

It often takes years until such findings are clinically implemented, even though the seed and knowledge had long existed.
Now, how does this hugely complex entity, the immune system, change with age?

Different immune populations shift in their own specific ways (no one-fits-all).

This review paper is a great resource summarizing ~200 references on #singlecell immune aging.

nature.com/articles/s4157… Image
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). Image
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. Image
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.

nature.com/articles/s4158…
2. Myeloid cell expansion

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.

nature.com/articles/s4158…
3. CD8+ T cells

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).
This comprehensive 2021 review article in @NatImmunol characterizes in detail the molecular features that define T cell aging.

nature.com/articles/s4159… Image
Next, this 2020 @ImmunityCP paper comprehensively characterizes immune aging across four different mouse tissues (spleen, peritoneum, lungs and liver) by both #scRNAseq and #scTCRseq.

cell.com/immunity/fullt… Image
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.

nature.com/articles/s4158… Image
Among many interesting findings, this large analysis suggests that CD8+ T cells become progressively exhausted with age.

Upon TCR stimulation, they produce pro-inflammatory molecules.
4. CD4+ T cells

Similarly to CD8, the amount of naive CD4 T cells decreases with age, while memory CD4 T cells increase.

Regulatory T cells increase, but not everywhere, rather only in some organs (s.a. spleen and lymph nodes), and not in others, (s.a. lungs or liver).
Interestingly, in supercentenarians, a specific population of cytotoxic CD4+ T cells are greatly expanded towards the end of life.

pnas.org/doi/full/10.10…
5. Stroma (non-immune) cells

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.

Multiple immune cell types interact with senescent cells: Tcells, NKs, neutrophils, macrophages.

Immune-mediated senescent cell removal could contribute to extending healthy aging & is therapeutically explored Image
How can anti-aging therapies use this complex information?

One promising route seems to be dietary interventions.

A 2020 @CellCellPress showed that caloric restriction triggers reshaped transcriptional landscapes in old rats🐀, including immune changes.

sciencedirect.com/science/articl… Image
The link between metabolism & the immune system is very interesting.

T cell metabolism undergoes heavy reprogramming with age.

This recent paper discusses in detail the mechanisms of these transformations, with particular relevance to tumor development.

cell.com/cell-metabolis… Image
Bonus Tweets 1/2:

The same publication I first referenced above (nature.com/articles/s4157…) also provides a comprehensive table with #singlecell #DataScience transcriptomic & epigenetic datasets of immune aging.

A great starting point for #Bioinformatics analyses! Image
Bonus Tweet 2/2:

Even more: they provide an interactive webpage to explore these (‼️preprocessed) datasets.

Kudos to the authors for this effort and for facilitating data access to so many relevant datasets #OpenScience👏

Good times to be grateful🎄

artyomovlab.wustl.edu/immune-aging/e…

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More from @simocristea

Dec 31, 2023
To end 2023, I’ll share one of the most insightful & well-written papers I read in 2023.

This study @Nature links *spatial* tumor organization to immunotherapy response in breast cancer.

Immunotherapy is our strongest weapon against cancer. We need to understand it better.
🧵🧵 Image
Long thread ahead, going deep into the molecular workings of breast cancer immunotherapy.

TL;DR:
1. Cancer–immune interactions & proliferative fractions predict immunotherapy response
2. Both pre-treatment & on-treatment predictors
3. Immunotherapy remodels the microenvironment
The paper is about triple-negative breast cancer (TNBC).

TNBC lacks ER & PR hormone receptors and human epidermal growth factor 2 (HER2) activity.

It is the most aggressive of the 4 breast cancer subtypes.

Responds poorest to treatment & has higher prevalence in younger women. Image
Read 41 tweets
Oct 31, 2023
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! Image
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 Image
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
Read 38 tweets
Sep 22, 2023
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🧵🧵 Image
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. Image
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.
Read 41 tweets
Jul 21, 2023
3 amazing papers just out @Nature, the kind worth giving up sleep for🦉

Spatial multi-omics human maps:
-placenta: MIBI & DSP
-intestine: CODEX & snRNAseq & snATACseq
-kidney: Visium & scRNA & scATAC

After sequencing single cells, we are now finally putting them back together🧵 Image
1. Placenta 1/5

Understanding the mysterious maternal processes that sustain embryo development is fascinating.

Mapping those spatially with proteins & mRNA to describe the maternal-fetal interface in the first half of pregnancy is really mind-blowing.

https://t.co/j14fWp8dZznature.com/articles/s4158…
Image
1. Placenta 2/5

- 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 Image
Read 19 tweets
Jun 20, 2023
Twitter messed up my previous thread, but this is too important to let it slide:

Here are (again) my summary & thoughts on early detection & an amazing work

Deep learning model trained on 9 million patient records in Denmark & US finds people at risk for pancreatic cancer
🧵🧵 Image
In this thread, we'll discuss:

1. Context & significance of study
2. Datasets
3. Deep learning model
4. Model performance
5. Feature interpretability
6. Thoughts

And here's the link to the paper:

nature.com/articles/s4159…
1. Context & Significance of Study
========================

Pancreatic cancer is a terrible disease.

Despite impressive progress, its 5-year survival rate in the US is currently no more than 12%. Image
Read 56 tweets
Jun 1, 2023
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. Image
It’s 1938, and Public Health Services are advising people that detecting and treating cancers early will save their lives. Image
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. Image
Read 46 tweets

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