Single cell metabolism: current and future trends

Abstract

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Keywords: Metabolic imaging; Single cell metabolism; Spatial metabolomics.

Figure 1.. Thin tissue slice of lung cancer- Digital Signal Processing

Adjacent fresh slices were incubated with ²H glucose+¹³C,¹⁵N Gln and analyzed by NMR and ultra-high resolution mass spectrometry. This slice (10 μm thickness) was stained for cancer (panCK), CD8 (T cells), CD68 (macrophages) expressing cells and DAPI (localizing nuclei). Circled regions (100 μm diameter) were further analyzed for the protein expression level of 58 different markers of cancer and immune cell functional states using oligonucleotide-barcoded antibodies using the NanoString Digital Spatial Profiling system. H&E stained sections showed the presence of other cell types including fibroblasts and endothelial cells. From Figure 4C of Fan et al. (Fan, Higashi, Song, Daneshmandi, Mahan, Purdom, Bocklage et al. 2021) under creative commons https://creativecommons.org/licenses/by/4.0/

Figure 2:. Schematic outline of the approaches used for isolating and preparing cells for single cell measurements

The workflow includes tissue sectioning and mounting for imaging native distributions of analytes, isolating specific cells, and cell dissociation. Adapted from the American Chemical Society (J. Am. Chem. Soc. 2017, 139, 11, 3920–3929).(Comi, Do, Rubakhin, Sweedler 2017) https://pubs.acs.org/doi/full/10.1021/jacs.6b12822 further permissions related to the material excerpted should be directed to the ACS

Figure 3:

Sampling a living cell with patch clamp pipette, enabling physiology, morphology and capillary electrophoresis mass spectrometry-based metabolomics on the same cell. Adapted from the American Chemical Society (Anal. Chem. 2014, 86, 6, 3203–3208). https://pubs.acs.org/doi/full/10.1021/ac500168d. Further permissions related to this material should be directed to the ACS

Figure 4.. Schematics of some strategies for ambient ionization techniques for single-cell metabolomics.

DESI- Desorption Electrospray Ionization; LDIDD- Laser Desorption/Ionization Droplet Delivery; ESI-electrospray ionization. Reproduced from Duncan et al. (Duncan, Fyrestam, Lanekoff 2019a) with permission from the Royal Society of Chemistry.

Figure 5.. Overview of organelle level metabolic characterization of cells.

Using the Raman Microscope, a single cell can be selected from the tumor microenvironment (A) and a more detailed image of organelle inside this cell can be obtained (B). Using our methodology, different classes of lipids in one organelle in different cell types can be quantified. Each organelle measurement is displayed with a different color (lysosomes in blue, mitochondria in green, Golgi apparatus in red and Endoplasmic reticulum in black. R132H is an active site mutation in isocitrate dehydrogenase, very prevalent in lower grade gliomas. From (Lita, Pliss, Kuzmin, Yamasaki, Zhang, Dowdy, Burks et al. 2021) with permission.

Figure 6.. Protocol for Raman-based quantification of lipids at the organelle level.

In Step 1, a green, fluorescent label is applied to the cells. In Step 2, organelles are localized and focused under microscope using this tag. In Step 3 and 4, Raman spectra are collected and BCAbox algorithm is applied to extract lipid profiles. In the last step, the unsaturation of lipid (LSU) is computed based upon the area ratio of 1655 cm⁻¹ to 1443 cm⁻¹. Via the same procedure, TCP parameter, which characterize trans/cis C=C bonds ratio in lipid species can be obtained from the intensities at 1666 cm⁻¹ and 1655 cm⁻¹. AG, Golgi apparatus, LD, lipid droplets. With permission from (Lita, Kuzmin, Pliss, Baev, Rzhevskii, Gilbert, Larion et al. 2019)

Figure 7.. Analytical techniques for metabolite identification and their evolution towards single cell metabolomics using either spatial dispersion methodologies or by imaging.

The width of each evolutionary line indicates the approximate number of instruments in the field (see key) and the color categorizes the certainty of metabolite identify from gold (full identification), silver (annotated) to bronze (class of metabolite, e.g. not specifically identified). LDI Laser Desorption Ionization; LAESI ablation electrospray ionization; SIMS secondary ion mass spectrometry; TERS tip-enhanced Raman spectroscopy.