Single-Cell Transcriptomics of Host-Pathogen Interactions During Viral Infection
Introduction
Viral infections at the cellular level are inherently heterogeneous. Within a single infected tissue, individual cells may harbor vastly different viral loads, exhibit distinct activation states of innate immune pathways, or remain entirely uninfected yet respond to paracrine signals from neighboring infected cells [1, 2]. Traditional bulk transcriptomic approaches average these signals across millions of cells, obscuring the discrete cellular states that govern infection outcome, viral dissemination, and host pathology [2, 3]. Single-cell transcriptomics, particularly single-cell RNA sequencing (scRNA-seq), has emerged as a transformative methodology for resolving this heterogeneity at the resolution of individual cells [1, 2, 4].
This review provides an exhaustive technical examination of how single-cell transcriptomic approaches are applied to study host-pathogen interactions during viral infection. The focus is on veterinary pathogens, including porcine reproductive and respiratory syndrome virus (PRRSV), African swine fever virus (ASFV), infectious salmon anaemia virus (ISAV), cyprinid herpesvirus, and avian influenza viruses, with comparative reference to well-characterized human viral systems where host-range parallels exist [5, 6, 3, 28, 31]. The discussion encompasses experimental design, computational analysis of viral transcript abundance, identification of bystander cell populations, mapping of immune response trajectories, and integration with spatial transcriptomics and proteomic data.
Technical Foundations of Single-Cell Transcriptomics in Virology
Droplet-Based and Plate-Based Platforms
Single-cell transcriptomic methods generally fall into two categories: droplet-based platforms that capture thousands to tens of thousands of cells by encapsulating individual cells in nanoliter-scale droplets, and plate-based platforms that sort individual cells into multiwell plates for downstream library preparation [2, 33]. Droplet-based methods offer higher throughput but often capture only the 3-prime end of transcripts, limiting the ability to detect viral splice variants or genomic segment distributions [7]. Plate-based methods, such as those employing full-length transcript coverage, enable more comprehensive characterization of viral RNA species, including quantification of individual viral genomic segments and subgenomic mRNAs [8, 7].
Virus-Inclusive Single-Nucleus RNA Sequencing
A specialized adaptation for viral infection studies is virus-inclusive single-nucleus RNA sequencing (viscRNA-seq), which simultaneously captures host and viral transcripts from individual nuclei [28]. This approach is particularly valuable for viruses that replicate in the nucleus, such as orthomyxoviruses and herpesviruses, and for tissues where enzymatic dissociation of intact cells is challenging [28]. In a study of infectious salmon anaemia virus (ISAV) infection in Atlantic salmon, viscRNA-seq revealed two distinct endothelial cell response patterns: one characterized by robust interferon-stimulated gene (ISG) expression and viral clearance, and another marked by suppressed antiviral signaling and high viral transcript abundance [28].
Quantification of Viral Transcript Abundance
A critical technical challenge in single-cell viromics is the accurate quantification of viral RNA molecules within individual cells. Standard scRNA-seq pipelines align reads to the host reference genome, discarding reads that map to viral genomes [7, 9]. Dedicated computational workflows have been developed to recover viral reads, quantify viral transcript abundance, and assign infection status to individual cells [7, 9]. For segmented viruses such as influenza A virus, these methods can determine the distribution of individual genomic segments across cells, revealing that most infected cells harbor incomplete segment sets and that the presence of all eight segments is rare [8]. This observation has profound implications for understanding the cellular basis of viral reassortment and the generation of defective interfering particles [8].
Heterogeneous Infection States and Bystander Cells
Defining Infection States
Single-cell transcriptomic data enable the classification of cells into discrete infection states based on viral RNA content, host transcriptional response, and surface marker expression [1, 3]. Common categories include productively infected cells (high viral transcript abundance), abortively infected cells (low or non-replicating viral RNA), bystander cells (no detectable viral RNA but altered host transcriptome due to paracrine signaling), and naive cells (no detectable viral RNA and no transcriptional response) [1, 2, 3].
In PRRSV-infected porcine lungs, scRNA-seq analysis identified that only a subset of alveolar macrophages, the primary target cell, supported productive viral replication [3]. Bystander macrophages and dendritic cells exhibited a distinct transcriptional program characterized by upregulation of pro-inflammatory cytokines and chemokines, including IL-1B, CCL2, and CXCL10, in the absence of detectable viral RNA [3]. This bystander activation contributes to the immunopathology observed in PRRSV infection and may explain the prolonged inflammatory response that predisposes pigs to secondary bacterial infections [3].
Spatial Organization of Infected and Bystander Cells
The spatial arrangement of infected cells relative to bystander populations is a critical determinant of tissue-level pathogenesis [5, 10]. Spatial transcriptomics technologies, which preserve the anatomical location of gene expression within tissue sections, have been applied to map the immune landscape of ASFV-infected porcine lungs [5]. Infected macrophages were found clustered in peribronchiolar and perivascular regions, surrounded by concentric rings of bystander neutrophils, natural killer cells, and T lymphocytes [5]. The transcriptional profiles of these spatially defined immune foci revealed gradients of type I interferon signaling, with cells proximal to infected macrophages exhibiting the highest ISG expression and cells at the periphery showing signatures of T cell exhaustion [5].
A similar spatial organization has been observed in coxsackievirus B3 (CVB3) myocarditis in mice, where whole-heart three-dimensional reconstruction combined with spatial transcriptomics identified discrete immune foci containing infected cardiomyocytes surrounded by activated macrophages and CD8+ T cells [11]. The transcriptomic heterogeneity within these foci correlated with local viral RNA abundance and predicted the eventual development of fibrotic lesions [11].
Host Immune Response Trajectory Mapping
Pseudotime Analysis of Antiviral Responses
Pseudotime algorithms, such as Monocle and Slingshot, order individual cells along a continuous trajectory based on transcriptional similarity, effectively reconstructing the temporal progression of cellular responses from a snapshot of gene expression [1, 2]. In the context of viral infection, pseudotime analysis can map the sequence of transcriptional events from initial viral sensing through interferon production, ISG induction, and eventual cell death or recovery [1, 12, 32].
In a study of Zika virus (ZIKV) infection in human trophoblast stem cells, pseudotime analysis revealed a biphasic host response [13, 12]. The early phase, occurring within the first six hours post-infection, was dominated by upregulation of pattern recognition receptors including RIG-I, MDA5, and TLR3, followed by a delayed wave of ISG expression peaking at 12 to 24 hours [13, 12]. Cells that failed to mount this biphasic response exhibited higher viral RNA abundance and were more likely to undergo apoptosis [13, 12]. This trajectory has been recapitulated in porcine alveolar macrophages infected with PRRSV, although the amplitude of the ISG response was markedly attenuated compared to ZIKV infection, consistent with the known immunomodulatory capacity of PRRSV [3].
Trajectory Divergence in Productive versus Abortive Infection
Single-cell trajectory analysis can distinguish the transcriptional paths of productively infected cells from those of abortively infected or resistant cells [1, 8, 31]. In a ferret model of avian influenza virus transmission, scRNA-seq of respiratory epithelial cells identified a bifurcation in the transcriptional trajectory [31]. Cells infected with a mammalian-adapted influenza strain progressed along a trajectory characterized by high viral polymerase activity, suppression of NF-kB signaling, and upregulation of lipid metabolism genes [31]. In contrast, cells infected with an avian-restricted strain diverged early, showing robust NF-kB activation, rapid ISG induction, and lower viral RNA accumulation [31]. This trajectory divergence was associated with the ability of the mammalian-adapted strain to suppress the host antiviral response at the single-cell level, a key determinant of transmission efficiency [31].
Viral Manipulation of Host Cellular Processes
Transcriptional Reprogramming by Herpesviruses
Herpesviruses, including cyprinid herpesvirus 2 (CyHV-2) in gibel carp and Epstein-Barr virus (EBV) in humans, extensively manipulate host transcriptional programs to establish productive infection and latency [6, 14]. Single-cell transcriptomic profiling of CyHV-2-infected carp spleen revealed that infected cells underwent a global shutdown of host mRNA transcription, a phenomenon known as host shutoff, mediated by the viral alkaline nuclease [6]. Concurrently, a subset of host genes involved in nucleotide biosynthesis and the ubiquitin-proteasome system were selectively upregulated, providing essential metabolites and proteasomal activity for viral replication [6, 14].
In EBV-associated nasopharyngeal carcinoma, meta-analysis of transcriptomic data identified the ubiquitin-proteasome system as a critical driver of tumor progression [14]. Single-cell analysis further resolved that EBV-infected epithelial cells exhibited heterogeneous expression of proteasome subunits, with cells expressing high levels of immunoproteasome components showing enhanced antigen presentation and reduced viral lytic gene expression [14].
Epigenetic Silencing of Viral Receptors
Host cells employ epigenetic mechanisms to restrict viral entry by silencing the expression of viral receptors [15]. The protein UHRF1, a key regulator of DNA methylation, was shown to restrict human coronavirus 229E (HCoV-229E) infection by epigenetically silencing the viral receptor aminopeptidase N (APN) [15]. Single-cell transcriptomic analysis of UHRF1-deficient cells revealed widespread demethylation of the APN promoter and increased cell surface expression of APN, rendering cells more susceptible to infection [15]. This mechanism may be conserved across species, as orthologs of UHRF1 in livestock species could similarly regulate receptor expression for coronaviruses such as porcine epidemic diarrhea virus (PEDV) and bovine coronavirus [15].
Integration with Multi-Omics Data
Pan-Viral Mapping of Host Dependency Factors
The integration of single-cell transcriptomic data with proteomic, metabolomic, and functional genomic datasets enables the systematic identification of host factors required for viral replication [16, 30]. A pan-viral map of host dependency factors was constructed by integrating scRNA-seq data from cells infected with influenza A virus, SARS-CoV-2, Zika virus, and dengue virus with machine learning-based multi-omics analysis [16]. This approach identified a core set of 47 host proteins that were required by all four viruses, including components of the COPI vesicle transport system, the proteasome, and the spliceosome [16]. Cell-type-specific dependency factors were also identified, explaining the differential tropism of these viruses across tissues [16].
For respiratory syncytial virus (RSV), a combination of scRNA-seq and genome-scale CRISPR screens defined the host dependencies of infection in human airway epithelial cells [30]. The screens identified the heparan sulfate biosynthesis pathway as a critical dependency, and single-cell transcriptomics confirmed that cells with high expression of heparan sulfate biosynthetic enzymes were preferentially infected [30]. This integrated approach has been extended to the cotton rat (Sigmodon hispidus), a key animal model for RSV, where single-cell transcriptomics revealed evolutionarily conserved host responses between rodents and humans [29].
Single-Cell Multi-Omics Databases
The growing volume of single-cell transcriptomic data from viral infection studies has motivated the development of specialized databases for data integration and reanalysis [4]. The scMOVIR database (single-cell multi-omics for viral infections and immune responses) provides a curated repository of scRNA-seq, single-cell ATAC-seq, and single-cell proteomics data from over 50 viral infection studies [4]. Users can query the database for cell-type-specific expression of viral receptors, interferon-stimulated genes, and immune checkpoint molecules across different viral infections and host species [4]. This resource facilitates cross-study comparisons and the identification of conserved host response modules [4].
Computational Methods for Viral Transcript Detection
Alignment and Quantification Pipelines
The detection of viral transcripts in scRNA-seq data requires specialized bioinformatic pipelines that can handle the high sequence diversity of viral genomes and the presence of both host and viral reads in the same sequencing library [7, 9]. A typical workflow involves the following steps:
- Preprocessing: Quality filtering, adapter trimming, and removal of low-complexity reads [7].
- Dual alignment: Simultaneous alignment of reads to a combined host-viral reference genome using splice-aware aligners such as STAR or HISAT2 [7, 9].
- Viral read classification: Assignment of aligned reads to specific viral genes or genomic segments based on sequence identity and coverage [8, 7].
- Quantification: Estimation of viral transcript abundance per cell, often normalized to host housekeeping genes or unique molecular identifiers (UMIs) [7].
- Infection status assignment: Classification of cells as infected or uninfected based on a threshold of viral reads, typically requiring at least two independent viral reads mapping to different regions of the viral genome [8, 7].
For segmented viruses, additional steps are required to determine the distribution of genomic segments across individual cells [8]. This involves quantifying the number of reads mapping to each segment and applying a binomial model to estimate the probability that a given cell contains a complete set of segments [8].
Quality Control and Artifact Detection
Viral reads in scRNA-seq data can arise from several sources beyond true cellular infection, including ambient viral RNA in the cell suspension, viral RNA bound to the cell surface without internalization, and sequencing artifacts [7, 9]. Stringent quality control measures are essential to distinguish genuine infection events from these artifacts [7]. These measures include:
- Removal of cells with extremely high viral read counts that may represent doublets containing multiple infected cells [7].
- Exclusion of cells where viral reads are concentrated in a single genomic region, suggesting a mapping artifact rather than authentic viral transcription [7].
- Comparison of viral read distributions across experimental replicates to identify batch-specific contamination [9].
Applications in Veterinary Vaccine Development
Identifying Correlates of Protection
Single-cell transcriptomics is increasingly used to identify cellular correlates of vaccine-induced protection against viral challenge [10, 33]. By profiling the immune response at single-cell resolution before and after vaccination, researchers can identify specific cell subsets and transcriptional programs that correlate with protection from subsequent infection [10, 33].
In the context of PRRSV vaccine development, scRNA-seq of porcine bronchoalveolar lavage cells after vaccination with a modified-live virus vaccine revealed that protection correlated with the expansion of a specific subset of CD163-negative alveolar macrophages that expressed high levels of ISGs and antigen presentation molecules [3]. These cells were absent in non-vaccinated pigs that developed severe disease upon challenge [3].
Mapping Antigen-Specific B and T Cell Responses
Single-cell transcriptomics combined with immune repertoire sequencing enables the simultaneous profiling of B cell receptor and T cell receptor sequences alongside transcriptional states [17, 33]. This approach has been used to track the clonal expansion and differentiation of antigen-specific B cells and T cells following viral infection or vaccination [17].
In a study of HIV and Mycobacterium tuberculosis co-infection, single-cell transcriptomics combined with T cell receptor sequencing revealed that co-infected individuals had a reduced frequency of Mycobacterium tuberculosis-specific CD4+ T cells and that the remaining antigen-specific cells exhibited a transcriptional program of exhaustion and senescence [17]. This finding has implications for veterinary co-infection models, such as PRRSV and Mycoplasma hyopneumoniae co-infection in swine, where similar immune dysregulation may occur [3, 17].
Integration with Structural Virology
Linking Receptor Expression to Cellular Entry
The expression level of viral entry receptors on the cell surface is a primary determinant of cellular tropism [15, 30]. Single-cell transcriptomics can quantify the expression of receptor-encoding genes across different cell types within a tissue, providing a transcriptional map of susceptibility [15, 30]. These data can be integrated with structural models of receptor-viral glycoprotein interactions to predict which cell types are most likely to support viral entry [15].
For example, the interaction between the ASFV glycoprotein p72 and its cellular receptor CD163 has been modeled using protein docking algorithms [5]. Single-cell transcriptomic data from porcine lungs showed that CD163 expression was highest on alveolar macrophages and that these cells were the primary targets of ASFV infection [5]. Cells with lower CD163 expression, such as interstitial macrophages and dendritic cells, were infected less frequently and supported lower levels of viral replication [5].
Three-Dimensional Visualization of Receptor Distribution
Advances in spatial transcriptomics and multiplexed imaging enable the three-dimensional visualization of receptor expression patterns within intact tissues [5, 11]. In the ASFV-infected porcine lung, three-dimensional reconstruction of CD163 expression revealed that receptor-positive macrophages were concentrated in the peribronchiolar interstitium, forming a network of susceptible cells that facilitated viral spread [5]. This spatial organization was disrupted in severe infections, where widespread macrophage depletion left large areas of the lung devoid of CD163 expression [5].
Workflow for Single-Cell Transcriptomic Analysis of Viral Infection
The following Mermaid diagram illustrates a typical computational workflow for analyzing host-pathogen interactions using single-cell transcriptomics.
flowchart TD
A[Single-cell RNA-seq data from infected tissue], > B[Preprocessing: Quality control, ambient RNA removal]
B, > C[Dual alignment to host + viral reference genomes]
C, > D[Quantification of host and viral transcript abundance]
D, > E[Assignment of infection status per cell]
E, > F{Infection status}
F, >|Infected| G[High viral RNA: Productive infection]
F, >|Infected| H[Low viral RNA: Abortive infection]
F, >|Uninfected| I[Altered host transcriptome: Bystander cell]
F, >|Uninfected| J[No transcriptional response: Naive cell]
G, > K[Pseudotime trajectory analysis]
H, > K
I, > K
J, > K
K, > L[Reconstruction of immune response dynamics]
L, > M[Identification of cell-type-specific responses]
M, > N[Integration with spatial transcriptomics]
N, > O[Three-dimensional mapping of infection foci]
O, > P[Validation with functional assays and proteomics]
Challenges and Future Directions
Technical Limitations
Despite its power, single-cell transcriptomics of viral infection faces several technical limitations [2, 33]. The low abundance of viral transcripts in many infected cells, particularly during early infection or in cells with abortive infection, makes detection challenging [8, 7]. Viral RNA can also be lost during the poly-A selection step used in many scRNA-seq protocols, as some viral RNAs lack poly-A tails [7]. Alternative protocols that use random priming or probe-based capture can mitigate this issue but may reduce sensitivity for host transcripts [7].
Computational Challenges
The computational analysis of single-cell viral transcriptomics data requires specialized tools that are not yet widely standardized [2, 4]. The accurate assignment of infection status, the handling of doublets containing cells from different infection states, and the integration of data across different viral strains and host species remain active areas of method development [2, 4].
Emerging Technologies
Emerging technologies such as single-cell multi-omics, which simultaneously profiles transcriptome, epigenome, and proteome from the same cell, promise to provide a more comprehensive view of host-pathogen interactions [4, 33]. Spatial transcriptomics at subcellular resolution, combined with viral RNA detection, will enable the mapping of viral replication complexes within individual cells [10, 11]. These advances will be critical for understanding the cellular basis of viral pathogenesis and for developing targeted interventions for veterinary viral diseases [33].
Conclusion
Single-cell transcriptomics has fundamentally advanced the study of host-pathogen interactions during viral infection by resolving the heterogeneity that is masked by bulk measurements. The ability to classify cells into discrete infection states, map immune response trajectories, and integrate transcriptional data with spatial and proteomic information has provided unprecedented insights into viral pathogenesis. For veterinary virology, these approaches are enabling the identification of cellular correlates of protection, the characterization of host dependency factors, and the development of more effective vaccines and therapeutics. As computational methods and experimental platforms continue to evolve, single-cell transcriptomics will remain an essential tool for dissecting the complex interplay between viruses and their hosts.
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