Harnessing the Potential of Multiomics Studies for Precision Medicine in Infectious Disease

Abstract

The field of infectious diseases currently takes a reactive approach and treats infections as they present in patients. Although certain populations are known to be at greater risk of developing infection (eg, immunocompromised), we lack a systems approach to define the true risk of future infection for a patient. Guided by impressive gains in "omics" technologies, future strategies to infectious diseases should take a precision approach to infection through identification of patients at intermediate and high-risk of infection and deploy targeted preventative measures (ie, prophylaxis). The advances of high-throughput immune profiling by multiomics approaches (ie, transcriptomics, epigenomics, metabolomics, proteomics) hold the promise to identify patients at increased risk of infection and enable risk-stratifying approaches to be applied in the clinic. Integration of patient-specific data using machine learning improves the effectiveness of prediction, providing the necessary technologies needed to propel the field of infectious diseases medicine into the era of personalized medicine.

Keywords: high-throughput technologies; infectious diseases; invasive fungal infections; systems immunology.

Figure 1.

Workflow of transcriptional genomics from patient samples through single-cell ribonucleic acid sequencing (scRNA-seq) (A) and spatially resolved transcriptomics (B). (A) Samples for scRNA-seq require dissociation of cells to ensure cells are not clumped together. Cells are sorted via antibody labeling to sort immune cells and nonimmune cells. Samples undergo reverse transcription and complementary deoxyribonucleic acid amplification and profiled by sequencing through selected sequencing technologies. These libraries often achieve 50000 reads, providing detailed readouts of cell populations and substates, T-cell receptor (TCR) and B-cell receptor (BCR) profiling, and underlying immune pathways in disease correlating with disease. (B) Tissue samples from infected regions, in this example lung tissue from lung transplant recipients infected with Aspergillus, are snap frozen and sectioned into thin slices for spatial transcriptomics. After permeabilization, tissues are exposed to probes designed to target specific RNA sequences followed by amplification that enables visualization of transcripts. Although methodology differs depending on the approach used, the current example of these probes requires a ligation and crosslinking with a fluorescent tag. The resulting data allow analysis of transcripts in their spatial location. BAL, bronchoalveolar lavage. (Printed with permission from Wolf N: 2021).

Figure 2.

Epigenomic approaches utilizing clinical local (eg, lung tissue and bronchoalveolar lavage [BAL]) and systemic (eg, blood) samples. (A) Assay for transposase-accessible chromatin using sequencing (ATAC-seq) measures chromatin conformation differences. The hyperactive transposase Tn5 loaded with a next-generation sequencing library enables fragmentation of open chromatin regions. These fragments are amplified and sequenced to provide physician-scientists with accessible chromatin regions at the single-cell level. (B) Histone modifications profiling through chromatin immunoprecipitation (ChIP) is an antibody-based technology that selectively enriches deoxyribonucleic acid (DNA)-binding proteins and their respective DNA targets (eg, histone modifications by methylation or acetylation). The DNA and its associated proteins on the chromatin are first crosslinked followed by fragmentation by sonication or a nuclease digestion. These fragments are then immunoprecipitated via antibody selection, which removes remaining cellular debris. Although there are multiple downstream analyses that can be run on ChIP precipitates, sequencing after DNA purification provides information on genome-wide binding in health and disease. (Printed with permission from Wolf N: 2021).

Figure 3.

Metabolite profiling through nontargeted approaches of known and unknown peaks. Patient samples for metabolomics require quick processing to extract metabolites prior to them being changed by biological mechanisms. Because the metabolome consists of molecules with very different physical properties, for example, both cationic and anionic compounds ranging from very polar to very nonpolar, it is necessary to devise distinct sample preparation and liquid chromatography-mass spectrometry (LC/MS) procedures to optimize metabolite coverage. These methods utilize different settings for separation via gas (gas chromatography [GC]) or LC step. Mass spectrometry analysis in the positive (C8-pos or hydrophilic interaction chromatography [HILIC]-pos) or negative (C18-neg or HILIC-neg) ion mode provides a wide array of metabolic peaks. These peaks can be compared with known metabolite library for identification. In addition to matches to known metabolites, there are often thousands of unknown peaks, which requires rigorous methodology to identify and authenticate metabolites. Identification and authentication approaches rely on tandem mass spectrometry (MS/MS)-based structure prediction as well as compound isolation and subsequent processing through GC or nuclear magnetic resonance spectroscopy (NMR) methodologies. Interrogation of the metabolome loops back to the patient by identification of metabolic biomarkers of disease. (Printed with permission from Wolf N: 2021).

Figure 4.

Workflow for mass cytometry (A), multianalyte array (B), and aptamer-based assay (C) proteomic approaches utilizing human patient samples. (A) To interrogate activated and inhibited pathways through phosphoproteomics, biological samples are briefly (15 minutes to 6 hours) stimulated with pathogen (eg, Aspergillus) of interest as well as with proper controls (eg, unstimulated and lipopolysaccharide). Stimulated samples are then incubated with metal-labeled antibodies targeting immune cells (cell surface antibodies), intracellular signaling proteins (phosphor-specific antibodies), and/or cytokines (intracellular cytokine antibodies). Cytometry by time-of-flight mass spectrometry (CyTOF) merges traditional flow cytometry with inductively coupled mass spectrometry to assess more than 50 simultaneously measured parameters on a cell-by-cell basis. (B) Targeted multianalyte arrays enable measurement of multiple proteins within a 96- or 384-well plate. Cell supernatants are put in individual wells containing color-coded beads precoated with antibodies for multiple analytes of interest. Detection antibodies for each target analyte as well as streptavidin-phycoerythrin (PE) are added for biotinylated detection. Detection and quantification of each analyte can be determined using a flow-based instrument or magnetic beads. Panels can be created to target specific secreted proteins. (C) Aptamer-based proteomics enables aptamers (eg, Slow Off-rate Modified Aptamers [SOMAmers]) labeled with a fluorophore, photocleavable linker, and biotin to be immobilized on streptavidin-coated beads and incubated with patient samples. After a biotin-tagging step, these aptamer-protein complexes are released by ultraviolet (UV) light-mediated photocleavage of the linker. The biotin labeled- aptamer-protein complexes are captured by a second set of streptavidin-coated beads and aptamers are released after incubation with denaturing buffer. A microarray chip is used to quantify fluorescence intensity within to total protein amount of the initial sample. Throughout this process, unbound proteins and nonspecific interactions are removed. BAL, bronchoalveolar lavage; LED, light-emitting diode. (Printed with permission from Wolf N: 2021).

Figure 5.

Workflow of reactive (A) and precision (B) infectious diseases (ID) medicine. A precision approach would enable clinicians to use preventative strategies, leading to fewer infections and infectious complications, including targeted prophylaxis or therapies that enhance immunity to specific pathogens. (C) Incorporation of multiomics approaches into an integrative machine learning model more accurately predicts clinical outcomes. Multiomics include transcriptomics (bulk or single-cell ribonucleic acid [RNA] sequencing [with or without paired analyses] and spatial transcriptomics), epigenomics (chromatin immunoprecipitation [ChIP] and assay for transposase-accessible chromatin using sequencing [ATAC-seq]), metabolomics (liquid chromatography tandem mass spectrometry [LC-MS] and one-dimensional proton nuclear magnetic resonance spectroscopy [NMR]), and proteomics (cytometry by time-of-flight [CyTOF], aptamer-based methods [SomaScan], and multianalyte array). Combining these multiomics methodologies across longitudinal samples of local and peripheral immune responses provides insight into relevant pathways and predicts clinical outcomes in disease. (Printed with permission from Wolf N: 2021).