1. INTRODUCTION
Transcriptomics, the study of the transcriptome (the total set of RNA molecules in a sample), provides valuable insight into cellular processes, gene expression, and regulatory mechanisms. Since its inception in the 1990s, the field has advanced significantly with the development of next-generation sequencing (NGS) and RNAseq that revolutionized gene expression study by enabling large-scale analyses and detecting previously unobtainable changes. This technology has expanded research on non-model organisms and species without prior genomic resources. In ecology, transcriptomics helps investigate ecologically important traits such as:
- resistance to challenging environments
- disease or infection resistance
- life history, development, and plasticity
- adaptation of species
When integrated with physiological data, transcriptomics offers a multidimensional approach to studying ecological questions.
2. LANDSCAPE TRANSCRIPTOMICS – THE ESSENTIALS
2.1. What is landscape transcriptomic
Landscape transcriptomics’ examines how genome-wide gene expression connects environmental variation to organismal function and genetic differentiation among populations. Landscape transcriptomics integrates principles from landscape ecology, macrosystems biology, landscape genomics, ecophysiology, and comparative transcriptomics.
Numerous reviews on landscape genomics have briefly mentioned the potential for other landscapeomics approaches (Fig. 2.1.) that go beyond studying DNA sequence variation. While each of these approaches can enhance our understanding of how populations respond to landscape-scale processes, transcriptomics offers a rich source of unique information while presenting some distinct challenges. Moreover, transcriptomic techniques have been refined to be high-throughput, standardized, and widely applicable across a range of organisms, making this approach more accessible than some other -omics approaches.
Figure 2.1. Omics—different approaches to gene expression (Source: Supplitt et al., 2021)
2.2. Landscape transcriptomics in ecology, evolution, and conservation
Landscape transcriptomics is uniquely positioned to answer an array of questions relevant to ecologists, physiologists, evolutionary biologists, and conservation biologists, which are addressed in the following sections
2.2.1. Transcriptome changes in compliance with environmental factors variations
Landscape transcriptomics combines landscape ecology with functional genomics and ecophysiology to understand how biotic and abiotic factors influence organismal responses over time. Population distributions are shaped by abiotic factors (e.g., temperature, precipitation, contaminants) and biotic factors (e.g., predators, competitors, diseases), which interact with gene expression in complex ways. Identifying differentially expressed genes, functional groups, and co-expression networks provides insight into molecular mechanisms driving biodiversity across spatial and temporal scales.
A correlation between phylogenetic relatedness and ecological similarity has been observed in both macro- and microorganisms. Closely related species share similar sets of genes (trait conservation), enabling survival under similar conditions (environmental filtering). Thus, genomes define fundamental niches and predict realized niches alongside environmental factors.
Scaling up transcriptomic studies to landscapes involves increasing replicates and minimizing confounding variables to improve inferential power, as shown in smaller-scale studies.:
- Parus major (Great tits) studies in rural vs. urban environments. Watson et al., 2017 have performed an assay aimed at the characterization of the transcriptomes from two distinct populations of great tits (Parus major): urban and rural. The study data revealed a big difference between the gene expression profiles of the target investigation sites, the blood, and the liver. The differentially expressed genes reflect the organism’s immune and inflammatory responses, detoxification, oxidative stress protection, lipid metabolism, and gene expression regulation. Additionally, it was found that genes linked to stress responses were expressed stronger in the urban population than in their counterparts from the rural one. These data conclude that the animals living under urban conditions are exposed to stronger environmental stressors and emphasize epigenetics as a mediator of environmentally-induced physiological variations. This pilot study can be considered a solid background for the design of further, in-depth research focused on the mechanisms that drive phenotyping variation in an urban context. In addition, knowledge is accumulated about the regulation of the phenotype at the molecular level in response to urban stressors in the wild.
- Bombus terricola(Bumble bee) studies in agricultural non-agricultural sites. The study performed by Tsvetkov et al., 2021 exploits a conservation genomics approach to study the population of the bumble bee (Bombus terricola) in a model system of agricultural and non-agricultural sites. Selected RNA samples from workers’ abdomens of both sites were sequenced, and the transcriptional profiles were identified. They showed an association with exposure to pesticides and pathogen infections. Further, meta-transcriptomic analyses were arranged, and they detected five pathogens, three common for the bumble and honey bees. These data provided functional support for the established negative role of the pesticides and pathogens spillover to the bumble bee populations. In addition, they prove the role of conservation genomics as a tool allowing quantification of the complex effects of multiple stressors on the pollinator populations in the wild nature.
2.2.2. Transcriptomic responses to environmental changes at population level
Research into population differences in environmental responses explores the repeatability of evolution, processes maintaining variation, and the role of plasticity in adaptive evolution. These insights responses are vital for addressing applied challenges such as assisted migration and population responses to habitat or climate change. Landscape transcriptomics aids species conservation and population management by linking genetic differences to environmental conditions, particularly in cases with genotype-by-environment interactions (GxE), where transcriptomics reveals how different genotypes respond to environmental factors. Populations may adapt via baseline gene activity or transcriptional plasticity, a key trait for flexibility, though transcriptomes’ dynamic nature poses challenges compared to DNA studies.
Studies on Anolis carolinensis and A. cristellatus identified temperature as a key driver of gene expression differences across environments. Adaptive shifts in cold tolerance were linked to respiratory efficiency, highlighting the value of pairing physiological data with transcriptomics.
2.2.3. Transcriptome – environment relations in conservation practices management
Quantifying the impact of the potential stressors is crucial for organismal biology, species management, and conservation. Short-term stress exposure, such as a pesticide, can cause lasting transcriptional changes influencing health, longevity, and fitness even if the stressor is no longer present. Furthermore, exposure to stressors can modulate gene expression in later generations after the stress has ended through various mechanisms.
Landscape transcriptomics efficiently evaluates responses of wild-caught specimens to acute, chronic, or prior stress exposure, helping identify and map stressors across large geographic regions. This approach allows for simultaneous monitoring of multiple stressors, distinguishing between different types of stress responses, and assessing long-term impacts through nonlethal biologically relevant methods. Therefore, transcriptomic signatures provide valuable data to guide management and conservation efforts..
2.2.4. Landscape transcriptomics in practical context
Advances in molecular tools and sequencing technologies over the past two decades have revolutionized the study of ecological and evolutionary processes in both model and non-model organisms. The decreasing cost of RNA sequencing (<$200 per sample) has made large-scale transcriptome sampling across landscapes financially feasible, enabling the integration of functional mechanisms with landscape-level processes.
Wild transcriptome sampling across environmental gradients links cellular responses to large ecological dynamics, critical for understanding species’ responses to stressors such as thermal changes, contaminants, and biotic interactions. While natural studies may complicate causal inference, combining them with controlled experiments offers powerful insight into environmental interactions. Landscape transcriptomics also informs policy and conservation efforts crucial for species affected by climate change and habitat disturbance.
Understanding species co-occurrence remains a central challenge in plant ecology. RNA sequencing enables detailed assessments of functional diversity and intraspecific variation, surpassing traditional trait-based approaches. Community transcriptomics compares functional gene similarities among coexisting species, addressing key questions about species co-occurrence in diverse ecosystems.
Modern metagenomic methods transform our understanding of plant-microbial interactions in the phyllosphere and rhizosphere. These techniques reveal microbiome diversity, spatial-temporal variations, and their influence on plant community structure and dynamics, offering insight into broader ecological patterns.
3. LANDSCAPE TRANSCRIPTOMICS OF WILD SYSTEMS
“Wild systems” generally refer to nonmodel organisms or nontraditional model organisms studied in their natural environments. These settings offer a unique opportunity to uncover gene expression patterns that cannot be observed in controlled laboratory settings.
Transcriptome studies in natural settings have uncovered novel activity in typically silent genes that respond to complex and dynamic environmental stimuli. Environmental challenges can reveal variations in gene expression among individuals and populations. For example, Whitehead et al. (2012) observed that differences in gene expression between pollution-tolerant and naïve F. heteroclitus populations only emerged when exposed to high toxin levels, while neutral processes explained non-responsive gene expression patterns. This fact highlights that environmental stress is crucial for exposing adaptive divergence, as controlled conditions may conceal these differences.
Transcriptome studies can also identify novel or unannotated transcripts in nonmodel organisms. In Daphnia pulex, unannotated genes accounted for over a third of the transcriptome and showed strong responses to ecological stimuli. The study identified gene duplication as a key mechanism driving rapid divergence, especially when duplicated genes interact within shared regulatory pathways. These findings emphasize that many gene functions remain unknown until organisms encounter complex natural conditions.
3.1. Studiesthrough whole-genome quantification techniques
DNA microarrays and RNA sequencing (RNAseq) are powerful tools for studying genome-wide gene expression in wild populations. These technologies analyze mRNA, which influences protein production and phenotypes shaped by ecological processes. By examining gene expression on a large scale, researchers can link molecular regulation to phenotypic responses to environmental factors, advancing understanding of the ecological transcriptome. These methods have addressed key questions, such as the extent of gene expression variation in nature, its environmental influences, and its role in shaping phenotypes, offering insight into the interplay between genetics and ecology. These two essential techniques are compared in Table 2.1. The brief protocols for their performance are summarized in Table 2.2 and Table 2.3.
Table 2.1 Comparison of Microarray and RNAseq Technologies
| Feature |
DNA microarray |
RNA sequencing |
| Age and popularity | Older and well-established; popular for its ease of analysis | Newer and increasingly popular for genome-wide transcriptomics |
| Method | Uses probes fixed to a surface to hybridize with fluorescent labeled cDNA | Utilizes next generation sequencing to read cDNA fragments derived from RNA transcripts |
| Platform dependency | Required pre-designed probes specific to known DNA sequences | Platform-specific sequencing differences in read length, depth, and quality must be considered |
| Input material | cDNA reverse-transcribed from mRNA labeled with fluorescent dyes | cDNA library generated from RNA transcripts; sequenced directly |
| Output data | Fluorescent intensity values proportional to cDNA abundance at each probe | High-throughput sequencing data generating millions of short reads |
| Analysis tools | Preprocessing includes normalization, logarithmic transformation, and statistical analysis of fluorescent signals | Data parsed using scripting languages; aligned to reference genomes or assembled de novo using software |
| Coverage | Limited to genes for which probes are available; cannot detect novel transcripts | Provides genome-wide coverage and can detect novel transcripts |
| Dynamic range | Narrower; may struggle with accurately quantifying very low or very high expression levels | Wider; better at detecting and quantifying low and highly expressed genes |
| Bias | Signal bias possible due to probe design and hybridization efficiency | Potential bias due to read depth, platform-specific errors, and library preparation steps |
| Applications | Ideal for focused studies on known gene sets | Suitable for exploratory studies and genome-wide transcriptome analyses |
| Cost and complexity | Lower cost and simpler workflows | Higher cost and more complex workflows, requiring computational resources |
Table 2.2 RNAseq Technologies basic protocol Source: https://www.youtube.com/watch?v=3hPyrQFTUuk&t=10s
|
Step |
Performance |
| What is RNA sequencing |  |
| The Central Dogma of molecular biology and RNA |  |
| RNA sequencing – generation of cDNA |  |
|
Step |
Performance |
| RNA sequencing – making RNA library: RNA fragmentation & 1st strand cDNA synthesis |  |
| RNA sequencing – making RNA library: RNA fragmentation & 2nd strand cDNA synthesis & A-tailing |  |
| RNA sequencing – making RNA library: adaptor ligation & amplification |  |
| RNA sequencing applications |  |
Table 2.3 DNA Microarray (DNA chip) basic protocol. Source: https://www.youtube.com/watch?v=NgRfc6atXQ8
|
Step |
Performance |
| What is DNA microarray |  |
| DNA Microarray sample preparation |  |
| DNA Microarray sample preparation –cDNA synthesis & fluorescent labelling |  |
|
Step |
Performance |
| DNA Microarray chip |  |
| DNA Microarray chip – oligonucleotide probe |  |
| DNA Microarray chip – sample loading |  |
|
Step |
Performance |
| DNA Microarray chip – sample 1 hybridization (gene X) |  |
| DNA Microarray chip – sample 2 hybridization (gene Y) |  |
| DNA Microarray chip – sample 1 + sample 2 hybridization (gene Z) |  |
| DNA Microarray detection |  |
3.2. Gene expression variation in evolutionary background
Variation in gene expression can be heritable and shaped by natural selection, reflecting both adaptive and nonadaptive processes. Regulatory elements or epigenetic mechanisms can influence expression before genetic variants arise, and population-level differences may signal early stages of adaptive divergence. Studies often use tests like Qst-Fst comparisons, McDonald-Kreitman tests, and quantitative trait loci (QTL) mapping to distinguish adaptive from neutral processes.
Both neutral and adaptive processes influence gene expression, and examining their roles in a phylogenetic framework can help us understand divergence at species or higher taxonomic levels. Comparing closely related species can reveal genes or regulatory changes important for speciation, particularly in response to environmental challenges. For example, Chelaifa et al. (2010) found differential expression in 13% of the transcriptome between Spartina maritima and Spartina alterniflora, including transporter, developmental, and cellular growth genes, which may support their ability to occupy distinct niches.
Studies have identified gene expression variation across time, space, and phylogenetic distance in natural environments. Advanced methods like Qst-Fst analysis quantify the roles of selection, drift, or bottlenecks in shaping expression patterns. Combining captive breeding resources (e.g., genetic maps) with natural population sampling enhances our ability to uncover the forces driving gene expression diversity, especially in diverging or recently diverged populations.
3.3. Gene expression variation in response to environmental stimuli
Understanding how organisms respond to environmental stimuli, including abiotic and biotic stressors, is critical in the context of climate change. Transcriptomics in natural settings has shown that even minor environmental changes can significantly affect gene expression, revealing GxE interactions and the molecular mechanisms of phenotypic plasticity. For example, studies in A. thaliana and other species have demonstrated that environmental factors like temperature, precipitation, and osmotic stress influence transcriptional variation across developmental stages.
Sampling consistency and timing are vital for reliable transcriptomic data. Gene expression is sensitive to temporal and environmental variables, requiring careful handling, preservation, and consideration of sampling times to avoid RNA degradation or batch effects. However, transcriptome assays provide only a “snapshot” of gene expression, necessitating time-course studies to capture dynamic regulatory processes, though these can be costly. Alternative approaches like qPCR for candidate genes offer a more practical way to track temporal changes.
Incorporating climatic change data into ecological transcriptomics helps identify drivers of gene expression variation, such as temperature and CO2 levels. Studies in crops like rice and soybean have modeled transcriptional responses to climate factors, revealing insights that could apply to wild populations, too. Advanced methods, including PCA and regression analysis, enhance our ability to link transcriptional changes to ecological contexts, improving predictions of species responses to climate change and environmental stressors.
3.4. Gene expression and phenotype relationship
For gene expression to play a functional role in ecology, it must affect phenotype. Few studies have confirmed the causal relationship between functional elements and phenotype through additional protein- or metabolism-based assays, by knocking out genes of interest, or through transgenic expression of genes of interest (Table 2.4).
Table 2.4 Key Studies and Advances in Ecological Transcriptomics: From Phenotypic Differentiation to Causal Gene Expression Links
| Category | Organism | Key findings | Reference |
| Alternate phenotypes | C. clupeaformis (lake whitefish) | Co-expression network analysis revealed divergent gene modules in brain and muscle tissues of normal and dwarf forms | Filteau et al., 2013 |
| Environmental disturbances | F. grandis
(gulf killifish) |
Post-oil spill, > 1,500 differentially expressed genes and altered gill morphology; cyp1a linked to developmental issues | Whitehead et al., 2011 |
| Stress adaptation | S. cerevisiae (yeast) | Adaptive fitness changes under salt stress linked to an SNP and genome size differences, revealing expression evolution in stress adaptation | Dhar et al., 2011 |
| Advanced ecological transcriptomics | S. cerevisiae (yeast) | Gene knockouts confirmed casual relationships between expression networks and traits | Zhu et al., 2008 |
To advance ecological transcriptomics, it’s crucial to move beyond correlations between gene expression and trait variation by incorporating manipulative techniques. Emerging tools like RNAi and CRISPR/Cas have already been applied to nontraditional models and could be expanded to ecologically relevant nonmodel species. Such approaches transition ecological transcriptomics from descriptive studies to those that elucidate processes, enhancing our understanding of genetic pathways and their diversification across taxa while informing environmental and evolutionary theory.
4. CHALLENGES AND PERSPECTIVES
4.1. Collection, analysis, and explanation of transcriptomics data
While landscape transcriptomics can address various questions, each with its considerations, several general challenges are being faced, like environmental stochasticity and individual variation, complicating the isolation of environmental effects on transcription. Factors like circadian rhythms, developmental age, sex, reproductive status, and immune/disease conditions, can obscure environmental responses. Controlling for these variables statistically or during sampling requires larger sample sizes and higher costs. However, the cost of sequencing continues to come down.
Populations may vary in transcriptional responses, necessitating good replication across transects or gradients. Sampling strategies must balance trade-offs: prioritize sites for landscape-driven questions or individuals for within-site variation. Simulation studies can help refine these trade-offs.
Pooling samples emphasizes population means but downplays individual variation, suiting some questions but not all. Decreasing sequencing costs improves study feasibility, but designs must match species and research goals, ensuring adequate replication and gradient representation.
In summary, how best to sample for landscape transcriptomic studies is an open and not trivial question that will depend to a large extent on the study question and species of interest. Below are summarized some specific issues and considerations related to landscape transcriptomics.
4.2. The gene expression as a time-based process
Most organisms have evolved biological rhythms to match temporal environment changes, regulating behavior, physiology, and gene expression. Circadian rhythms in gene expression are broadly found across a wide range of taxa, from prokaryotes to plants, fungi, and animals. For example, intertidal mussels and reef-building corals modulate their gene expression in response to circadian and tidal rhythms and seasonal and lunar cycles. These findings emphasize the need for experiments that account for temporal environmental changes across geographic regions.
Scale is central to landscape ecology. Landscape emphasizes spatial heterogeneity, where the concept of “landscape” varies by organism size and dispersal ability. Small-scape environments, like those of microbes, offer experimental models for studying landscape transcriptomics relevant to both microbes and larger organisms.
4.3. Tissue specific responses and transcriptomics studies
When performing studies on multicellular organisms, another essential consideration is tissue choice. Tissue-specific differences in gene expression can be substantial and may differ across populations. For example, in a study of Atlantic killifish, Fundulus heteroclitus, 76% of metabolic genes were differentially expressed among brain, heart, and liver tissues. Of these, only 31% of tissue-specific differences were consistent in expression among fish, originating from three populations along the U.S. east coast. Even within a single organ, differences between cell types of that tissue can be substantial. Therefore, it is necessary to evaluate carefully, which tissue is chosen and acknowledge the limits of inference created by that choice.
4.4. The future of landscape transcriptomics
Microarrays were the dominant method for whole-genome transcription quantification for a decade, but RNAseq use has surged recently, particularly for species without prior genomic resources. Microarrays, although less common, remain valuable in study designs, e.g., for studying stimuli response and gene expression patterns, particularly if already available. Combining microarrays and RNAseq can test global gene expression patterns in natural populations.
Epigenetic mechanisms, such as DNA methylation, can influence gene expression in response to environmental factors. While DNA sequence differences can be context-dependent, DNA methylation is more susceptible to environmental influence, with natural settings potentially revealing alternative methylation profiles unseen in controlled experiments. Combining RNAseq with DNA methylation assays may provide deeper insights into the genetic architecture of environmental responses in landscape-scale studies.
Proteomics will complement transcriptomic studies by linking gene expression to functional protein products, helping better understand how environmental stressors translate into phenotypic changes at the landscape scale.
5. CONCLUSION
Landscape transcriptomics combines functional genomics with landscape ecology to understand how organisms respond to changes in environmental factors at a molecular level across various spatial and temporal gradients. This field has evolved rapidly over the past decade, utilizing technologies like RNAseq and microarrays to measure genome-wide gene expression in wild populations. This approach reveals gene-environment interactions, shedding light on adaptation to stressors and environmental changes, which is crucial in the context of climate change.
However, challenges remain, especially for nonmodel species that lack genomic resources. Future research will likely combine transcriptomics with proteomics and epigenetics to better understand environmental responses. This integration will be key to understanding how organisms adapt to their environments and informing species conservation strategies. Landscape transcriptomics offers valuable insight into ecological and evolutionary processes, making it a promising tool for conservation and management efforts.
Proteomics will complement transcriptomic studies by linking gene expression to functional protein products, helping better understand how environmental stressors translate into phenotypic changes at the landscape scale.
Traditional model organisms like Mus musculus, Saccharomyces cerevisiae, Drosophila melanogaster, and A. thaliana benefit from extensive genomic resources, including gene annotations and predicted interactions. However, ecologists often study nonmodel organisms that lack these resources. Researchers rely on annotations from the closest model relative, but genetic divergence can render these annotations inaccurate. Even in model organisms, entire genomes remain incompletely annotated. Developing species-specific genomic databases and improving annotations will be crucial for advancing landscape transcriptomic studies.
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