Metabolomics Software Offers A Path Forward In Immunometabolism
Until recently, the metabolome of immune cells has been a topic non grata. It’s no secret that immune cells have internal metabolic processes like all other cells. If pressed, most immunologists would admit that there’s probably some interaction between an immune cell’s local environment and its metabolism. The difficulty is that once immunologists admit that an immune cell is heavily influenced by its surrounding non-immune cells and access to nutrients, an entire can of worms is open: immunometabolomics. From an immunologist’s perspective, invoking metabolomic explanations for immune cell phenomena is akin to Carl Sagan’s famous dictum: “To make an apple pie from scratch, you must first invent the universe.” While comprehensive, metabolomics tends to complicate origins of phenomena far more than it explains, and by necessity introduces a vast amount of variables which must be accounted for to arrive at any sensible experimental conclusion. In a new review published in Cell, several leading immunologists make the case for diving into this topic using software as an aid in order to unlock immune cell metabolism and gain insights into homoeostasis and cancer treatment.1
Why Is Immunometabolomics More Difficult Than General Metabolomics?
To most bench biologists, immunometabolomics doesn’t appear any more difficult to incorporate into experimental practice and hypothesis formation than traditional metabolomics. Indeed, practically every immunology experiment incorporates at least a little bit of metabolomics by technical means like using an artificial cellular media. The standard experiment recognizes “normal” immune cell metabolism as a precondition for testing other features of the cell. In experiments on immune cell metabolism specifically, the picture isn’t that different.
Incorporating metabolomics into an in vitro immunology experiment poses the following obvious challenges:
- Mapping metabolic processes relevant to the experiment
- Mapping predicted changes of metabolic processes after experimental intervention
- Providing a cell media which supports the immune cells in both control and experimental conditions without disrupting the rest of the experimental apparatus
- Characterizing immune cell metabolome in control condition experimentally
- Characterizing immune cell metabolome in experimental conditions experimentally
This set of challenges is formidable, but manageable if researchers are willing to dedicate themselves. Unfortunately, this picture is sorely incomplete because it relies on the abstraction of a “normal” immune cell metabolism. In the review, the authors maintain that while useful for basic experimentation, there is in fact no single “normal” immune cell metabolism because immune cell metabolism is influenced by an abundance of external factors.
As the authors of the review discuss in detail, standard in vitro methods are unlikely to render any deep insights into immunometabolic phenomena because they’re missing a critical set of components that exist in vivo:
- Fluctuation of nutrient levels in the matrix surrounding the immune cells when infiltrated into destination tissue
- Local nutrients which may inhibit immune cell activity by design or pathologically2
- Colocation of immune cells with tissue-specific somatic cells
- Intercellular signalling mixture secreted from the tissue specific somatic cells
- Physiological conditions like stress or sleep which are transient, but drastically impact immune cell function3
- Variable concentration of immune cells and somatic cells dependent on tissue location
- Variable access to circulating nutrients, other immune cells, and other cells
- Radical genomic changes caused by the summation of factors at the immune cells’ destination4
Now, the entire picture is visible: immune cells found in various physiological contexts are drastically different from their correlates elsewhere, and there’s no median immunometabolomic profile. Given how core metabolism is to immune cell function, immunmetabolomics is a field that’s about to take off if researchers can access a data management platform to help keep track of all the variables involved.5
Mapping The Immunometabolome
While research has a long way to go before fully fleshing out all of these components, the authors of the review join others in pointing to the potential to learn more about cancer cell metabolism’s impact on immune cells to argue that the future of immunology incorporates immunometabolism at a deep level.6 7 Indeed, the review contends that rejecting the “normal” immune cell metabolism abstraction will be necessary to bridge the gaps between knowledge gained in vitro, in vivo, and in human in vivo clinical studies.
It’s clear that researchers dipping their toes into the immunometabolomic waters aren’t logistically ready for the challenge until they have a way of collating all the relevant data. Exploring the differences between immune cell metabolomic profiles in different physiological contexts and how those differences influence effectiveness requires opening up several new fronts of data management which simply weren’t touched before. Laboratory information management systems which frequently fumble variables by having a weak tagging functionality will be a huge barrier to research within immunometabolomics. As the authors of the review claim, this research is simply too complicated to undertake unless you have computer assistance to make metabolic maps, track functional assays, and help inform predictions about experimental interventions in order to hone a hypothesis.
Furthermore, a unitary laboratory information solution will be required; spreading out data into different systems is all too often a consequence of multifunctional research, as each type of data has peculiarities which a single system might not handle gracefully. The largest wealth of insights from immunometabolomics will come from comparisons between data sets with different origins. Luckily, there is a software platform which is powerful enough to handle the multitude of diverse data sets which immunometabolomic research will generate and also require for initiation.
Designed to Cure is the software platform that your laboratory will need to dive into immunometabolomics. Using Designed to Cure, you’ll be able to plan experiments, track dozens of variables, compare your data to other data sets, and share everything with collaborators. Contact us today to find out how we can help you jump into the new wave of immunometabolomics.
- “Metabolic Instruction of Immunity.” April 2017, http://www.cell.com/cell/fulltext/S0092-8674(17)30416-6. ↩
- “Immune Cell Intolerance For Excess Cholesterol.” December 2016, http://www.cell.com/immunity/fulltext/S1074-7613(16)30488-5. ↩
- “Anabolism-Associated Mitochondrial Stasis Driving Lymphocyte Differentiation Over Self-Renewal.” November 2016, http://www.cell.com/cell-reports/fulltext/S2211-1247(16)31639-4. ↩
- “Germinal Center Hypoxia Potentiates Immunoglobulin Class Switch Recombination.” November 2016, https://www.ncbi.nlm.nih.gov/pubmed/27798169?dopt=Abstract. ↩
- “T Cell Metabolism Drives Immunity.” August 2015, http://jem.rupress.org/content/212/9/1345. ↩
- “Metabolic Competition in the Tumor Microenvironment is a Driver of Cancer Progression.” August 2015, http://www.cell.com/cell/abstract/S0092-8674(15)01029-6. ↩
- “LDHA-Associated Lactic Acid Production Blunts Tumor Immunosurveillance by T and NK Cells.” August 2016, http://www.cell.com/cell-metabolism/abstract/S1550-4131(16)30427-2. ↩