Linking Leaf Hyperspectral Reflectance and Gene Expression

Linking Leaf Hyperspectral Reflectance and Gene Expression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Brief Communication Linking Leaf Hyperspectral Reflectance and Gene Expression Nathan Swenson, Yanni Chen, Logan Monks, Vanessa Rubio This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5566913/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Aug, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract Hyperspectral reflectance data are utilized in ecology to predict functional trait values, but the diversity of functions captured by these traits is limited. Here, we demonstrate a novel integration of reflectance and to gene expression data for processes of interest to ecologists. We show linkages between the expression of ecologically important genes and reflectance data and the potential to transform the depth at which ecologists can rapidly estimate functional diversity. Biological sciences/Ecology/Forest ecology Earth and environmental sciences/Ecology/Forest ecology spectroscopy transcriptomics forest ecology functional traits functional ecology Figures Figure 1 Introduction Forests store ~ 80% of the biomass and biodiversity on the planet and account for ~ 75% of gross primary productivity on the terrestrial surface ( 1 , 2 ). The structure, composition and health of these ecosystems are the net result of the successes and failures of individuals and species in a rapidly changing environmental context. Trees interface with their environment via their functional biology, often quantified via functional traits. Functional traits are relatively easy to measure traits representing where species fall along major tradeoff axes related to plant form and function and they offer a pragmatic approach for understanding how individuals to ecosystems interface with a changing world ( 3 ). Despite the widespread use of functional trait-based approaches in plant ecology, they have been, traditionally, limited in two ways ( 4 ). First, quantifying trait data across individuals and/or broad spatial extents is challenging despite their relative ease of measurement. That is, researchers often have to estimate the trait value of all conspecifics within or across populations using a species-level mean trait value and/or have only been able to quantify these traits on the scale of typical forest plot (e.g. 0.1–0.5 km 2 ) ( 4 , 5 ). Second, while the core set of functional traits typically measured in plant ecology does provide insights into key tradeoffs, they do not capture the breadth of the functional diversity within plants. That is, they may provide only topical insights into how trees, for example, are responding to key drivers of forest dynamics and health (e.g. drought, pest and pathogen outbreaks). Advances in remote sensing leveraging imaging spectroscopy are quickly removing the first limitation by providing maps of functional traits at broad spatial extents and fine spatial resolutions ( 6 , 7 ). For example, models of leaf traits based upon their reflectance have been applied to hyperspectral imagery for thousands of acres to entire countries to provide detailed predicted maps of leaf traits that are revolutionizing forest ecology ( 6 ). Functional genomics holds the potential to overcome the second limitation ( 8 ). For example, gene expression data provide broad and deep assays of plant functional diversity within and across individuals and species ( 9 , 10 ). However, the integration of functional genomics into forest ecology has lagged that of hyperspectral biology partly due to the cost of data acquisition and the associated challenge of quantifying gene expression on large numbers of individuals. Here, we provide evidence that the hyperspectral reflectance of tree leaf tissue is strongly related to the expression of thousands of expressed genes in two ecologically dominant and economically important maple ( Acer ) tree species from North America. The findings demonstrate the potential for spectral reflectance data to provide rapid and deep functional genomic assays on broad spatial extents that could transform forest ecology. Results and Discussion The present study investigated the degree to which gene expression is linked to the spectral reflectance of leaf tissue as a means of potentially expanding the depth and breadth of plant function that can be inferred from hyperspectral imaging data. To this end, we used a regularized canonical correlation approach using leaf tissue from naturally occurring individuals from two of the most common and economically important tree species in eastern North America - sugar maple ( Acer saccharum [Sapindacae])( n = 14 ) and red maple ( A. rubrum )( n = 12 ). The analyses found evidence of strong correlations between the measured range of spectral reflectance (400–2400 nm) and the expression of genes related to key ecological functions (Fig. 1 ). Furthermore, when comparing the results across species, similar spectral ranges were the most likely to correlate with gene expression (Fig. 1 a, 1 b). The analyses allowed us to investigate a broad range of expressed genes in the leaf tissue, but we were particularly interested in genes that were annotated with functions that are ecologically important and potentially challenging to assess using commonly measured functional traits. For example, we identified genes annotated to three key functions (plant-water relations, abscisic acid (ABA) response, and photosynthesis) that have strong canonical correlations (> 0.7) in the near-infrared (NIR) and shortwave infrared (SWIR) wavelengths (Fig. 1 c, 1 d). Other pathogen, drought-specific and pigment related genes had moderately strong (0.5–0.7) canonical correlations between their expression and reflectance (Fig. 3f, 3h). Importantly, genes identified in the same functional group and even in the same biological pathway may be differentially correlated with reflectance spectra owing to differences in the up and down regulation of genes. Further, while some wavelengths may have strong (> 0.7) canonical correlations with the expression of a gene, there are typically multiple additional wavelengths with moderate canonical correlations with expression. These moderate to strong canonical correlations from multiple wavelengths indicates that robust predictive models of gene expression from reflectance data can be generated using a larger dataset. Predictions of functional trait values from spectral data leverage trait-reflectance relationships from a broad range of wavelengths ( 11 ). This finding demonstrates the capacity for leaf spectral reflectance data to predict the expression of functionally important genes. Next, we sought to investigate the relative ability of different spectral ranges to predict gene expression. The three broad wavelength ranges (i.e. visible [VIS], NIR and SWIR) varied in the degree to which they correlated with gene expression. Despite the known utility of VIS wavelengths to predict phenotypic traits ( 12 , 13 ), our findings indicate that they have a reduced capacity to predict the expression of the genes studied. In both A. saccharum and A. rubrum , the highest magnitude loadings for reflectance in the regularized canonical correlation analysis were associated with the NIR wavelengths (Fig. 1 e, 1 g). Specifically, NIR wavelengths can contribute to predictions of gene expression related to ABA, pathogens, photosynthesis, pigments and plant-water relations. Loadings from the SWIR range made both moderate and high contribution (Fig. 1 f, 1 h). The SWIR wavelengths contributed to correlations of every category of gene function we annotated in our study including drought-specific genes (Fig. 1 f, 1 h). The increased ability of SWIR, relative to NIR, to correlate with the expression of drought-specific genes is supported by previous studies that have noted the importance of both NIR and SWIR wavelengths for assessing the drought status of vegetation ( 14 , 15 ). In summary, we find that NIR wavelengths tended to have relatively more linkages to the expression of genes, but the SWIR wavelengths have relatively stronger associations with some groups of genes and are likely critical additional information for producing robust predictions of gene expression that leverage both the NIR and SWIR wavelengths. Conclusions Here, we have shown that the expression of genes in leaf tissue across two tree species are associated with leaf-level spectral reflectance. This finding indicates that ecologists may be able to dramatically expand their capacity to generate functionally deep assays of function across broad spatial extents. However, there are some considerations going forward. First, roughly half of the genes in this study were correlated with spectral reflectance. Thus, this approach will not offer the ability to predict the expression of all genes or functions of interest to an ecologist. Second, predictive approaches like those used in the functional trait literature (e.g. partial least square regressions) are improved by large numbers of samples and, ideally, from individuals experiencing a broad range of environments. Lastly, we have focused only on the genes that can be reliably annotated or identified via gene ontologies. This results in a large number of genes that are not analyzed but are likely to have important functions. Despite these challenges, the potential for predicting the expression of hundreds to thousands of ecologically important genes from spectral reflectance is immense. Materials and Methods Leaf tissue from naturally co-occurring adult canopy trees was collected at the University of Notre Dame Environmental Research Center. Leaf spectral reflectance data and mRNAseq from sun-exposed leaf tissue were collected using standard protocols and analyzed in unison using regularized canonical correlations. Further details are in SI Appendix. Declarations Data, Materials, and Software Availability . All study data and data processing are included in the article and/or SI Appendix. Acknowledgments This research was funded by the Biodiversity and Ecological Conservation Program at NASA (Grant No. 80NSSC22k1625). The authors are thankful for the Bernard J. Hank Family Endowment, which funds facilities, education and research at the University of Notre Dame Environmental Research Center. Author Contributions: NGS conceived of the research idea. YC, LM, VER, and NGS collected the data. YC, LM and NGS conducted the data analysis and wrote the manuscript. References C. Beer et al., Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329 , 834-838 (2010) Y.D. Pan, R.A. Birdsey, O.L. Phillips, R.B. Jackson, The structure, distribution, and biomass of the world’s forests. Annu. Rev. Ecol. Evol. Syst. 44 , 593-622 (2013) P.B. Reich et al., The evolution of plant functional variation: traits, spectra, and strategies. Int. J. Plant Sci. 164 , S143-S164 (2003) J. Yang, M. Cao, N.G. Swenson, Why functional traits do not predict tree demographic rates. Trends in Ecol. Evol. 33 , 326-336 (2018) J.L. Osnas et al., Divergent drivers of leaf trait variation within species, among species, and among functional groups. Proc. Nat. Acad. Sci. 115 , 5480-5485 (2018) G.P. Asner et al., Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355 , 385-389 (2017) Z. Wang et al., Foliar functional traits from imaging spectroscopy across biomes in eastern North America. New Phyt. 228 , 494-511 (2020) N.G. Swenson, F.A. Jones, Community transcriptomics, genomics and the problem of species co-occurrence. J. Ecol. 105 , 563-568 (2017) M. Alvarez et al., Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution? Molec. Ecol. 24 , 710-725 (2015) N.G. Swenson et al., Tree co-occurrence and transcriptomic response to drought. Nat. Comm. 8 , 1996 (2017) A.C. Burnett et al., A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression. J. of Exp. Bot. 72 , 6175-6189 (2021) D. A. Sims, J. A. Gamon, Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing Environ. 81, 337-354 (2002). B. Datt, Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a + b, and total carotenoid content in Eucalyptus leaves. Remote Sensing Environ. 66, 111-121 (1998). G. A. Carter, Primary and secondary effects of water content on the spectral reflectance of leaves. Amer. J. Bot. 78 , 1916-1924 (1991). J. Peñuelas, I. Filella, C. Biel, L. Serrano, R. Save, The reflectance at the 950-970 nm region as an indicator of plant water status. Int. J. Remote Sensing 14 , 1887–1905 (1993) Online Methods and Data Deposition Field Data Collection: Sun-exposed canopy leaves were collected from 14 sugar maple ( Acer saccharum [Sapindaceae]) and 12 red maple ( A. rubrum ) trees at the University of Notre Dame Environmental Research Center located on the border of Wisconsin and the upper peninsula of Michigan. The leaves were collected prior to 11am over a three-day period during June 2023. A sample of fully-expanded leaves with no or minimal damage was flash frozen in liquid nitrogen immediately in the field to preserve RNA. Adjacent leaves similar in condition on the same branch were collected for spectral reflectance measurements. Spectral reflectance was measured on the adaxial leaf surface using an ASD FieldSpec 4 Hi-Res spectroradiometer and an attached ASD integrating sphere (Malvern Panalytical Ltd., Malvern, U.K.). In accordance with the protocol provided in the ASD integrating sphere manual, we collected measurements of the reference standard between sample leaf reflectance measurements as well as measurements of stray light. During measurements, care was taken to avoid the midrib and other major venation. Measurements were taken from one healthy leaf per individual comparable to the leaf tissue frozen from the same branch. Five repeated spectral measurements were taken of the leaf and averaged for downstream analyses. Due to noise inherent in reflectance measurements at extreme wavelengths, the final data set was trimmed to only include reflectance values between 400 and 2400 nm. RNA Extraction, Sequencing and Bioinformatic Analysis: The frozen leaf tissue was stored in 10 ml cryovials in a -80°C freezer prior to RNA extraction. In the lab, tissue was hand ground into small pellets and transferred into a 2 ml tube with 2.8 mm ceramic beads to further grind the tissue power via a Qiagen TissueLysser II (Qiagen, Hilden, Germany). The totalRNA of each leaf sample was extracted using Qiagen RNeasy Plant Mini Kits via a Qiagen QIACube Connect using the standard kit protocols. The totalRNA samples were sent to Novogene (Davis, California) for sequencing using an Illumina NovaSeq X platform. The mRNA sequencing read data are paired-end 150bp with ~4 GB per sample. Sequencing reads were trimmed with fastp (1), and measured quality before and after data trimming using fastqc (2) and multiqc (3). We mapped trimmed reads to a published chromosome-scale A. saccharum genome (4) using hisat2 (5) and summarized reads coverage with samtools (6) and stringtie (7). We also leveraged the existing annotation associated with the published A. saccharum genome and annotated unknown genes with blastx (8), blastp (8), hmmscan (9). All gene and transcript annotations were wrapped together with Trinotate (10). Gene expression levels of each sample were summarized using transcript-level expression. It is important to note that we analyzed gene-level expression via summarized transcript-level expression, which may lead to “cancelation effects” in the RNAseq data. Data Integration: Gene expression data for all samples were summarized into a single data matrix per species. In this analysis, we only analyzed genes with annotations that contained the words: ABA, drought, pathogen, photosynthesis, pigment or water for A. saccharum ( n = 590 ) and A. rubrum ( n = 1219 ). We focused on these general terms as they are linked to functions and processes of interest to forest ecologists. We note that other annotated or unannotated genes are ecologically important and could or should be analyzed by ecologists when they fully employ the approach presented in this report. Spectral reflectance measurements of all samples were summarized as a data matrix. To quantify the maximum correlation between the two groups of variables, we implemented a regularized canonical correlation analysis via the mixOmics package (11) in R (12). We optimized the regulatory parameters using a parallel version of the tune.rcc() function. Next, we calculated the loadings of each variable in each analysis under the optimal lamda value calculated. Last, we calculated and plotted the canonical correlation of each variable. Data Deposition: Sequencing reads used in this project were uploaded to BioProject (ID PRJNA1183736) in NCBI short reads archive. Leaf reflectance data and data analysis scripts were stored in a public Github repository (chenyanniii/spectral_trans). SI References 1. S. Chen, Y. Zhou, Y. Chen, J. Gu, fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34 , i884-i890 (2018). 2. S. Andrews, Others, FastQC: a quality control tool for high throughput sequence data. [Preprint] (2010). 3. P. Ewels, M. Magnusson, S. Lundin, M. Käller, MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32 , 3047-3048 (2016). 4. S. L. McEvoy, et al. , Strategies of tolerance reflected in two North American maple genomes. Plant J. 109 , 1591–1613 (2022). 5. D. Kim, J. M. Paggi, C. Park, C. Bennett, S. L. Salzberg, Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37 , 907-915 (2019). 6. P. Danecek, et al. , Twelve years of SAMtools and BCFtools. Gigascience 10 , giab008 (2021). 7. M. Pertea, D. Kim, G. M. Pertea, J. T. Leek, S. L. Salzberg, Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11 , 1650-1667 (2016). 8. C. Camacho, et al. , BLAST+: architecture and applications. BMC Bioinformatics 10 , 421 (2009). 9. S. R. Eddy, Accelerated profile HMM searches. PLoS Comput. Biol. 7 , e1002195 (2011). 10. D. M. Bryant, et al. , A Tissue-Mapped Axolotl De Novo Transcriptome Enables Identification of Limb Regeneration Factors. Cell Rep. 18 , 762-776 (2017). 11. K.-A. Lê Cao, Z. M. Welham, Multivariate data integration using R: Methods and applications with the mixOmics package (Chapman and Hall/CRC, 2021). 12. R Core Team, R: A Language and Environment for Statistical Computing. (2024). Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 23 Aug, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5566913","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Brief Communication","associatedPublications":[],"authors":[{"id":386293607,"identity":"8f9cddf5-d81b-44bf-8b11-5427574ef668","order_by":0,"name":"Nathan Swenson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYDADfgYGAwYGNlK0SDaQrMXgALFazPnPGD4u+GOTZ3y7eQPDh7LDhLVYzsgxNp7ZllZsdudYAeOMc0RoMbjBu02at+Fw4rYbOQbMvG3EaDl/dvtvnj+HEzfPAGr5S5SWA7nbmHnYDidukABqYSRKy438z9K8bWmJM26kFRzsOZdOjMOOJX7m+WOT2D8jeeODH2XWhLWggAMkqh8Fo2AUjIJRgAsAAOj6PMjttIjOAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3819-9767","institution":"University of Notre Dame","correspondingAuthor":true,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Swenson","suffix":""},{"id":386293608,"identity":"d342e3a5-dda3-410f-8788-7d000ab60ae8","order_by":1,"name":"Yanni Chen","email":"","orcid":"","institution":"University of Notre Dame","correspondingAuthor":false,"prefix":"","firstName":"Yanni","middleName":"","lastName":"Chen","suffix":""},{"id":386293609,"identity":"bfa28f68-1ba6-4020-b866-3531d6d04714","order_by":2,"name":"Logan Monks","email":"","orcid":"","institution":"University of Notre Dame","correspondingAuthor":false,"prefix":"","firstName":"Logan","middleName":"","lastName":"Monks","suffix":""},{"id":386293610,"identity":"302572e9-a128-4760-9305-0134178463a4","order_by":3,"name":"Vanessa Rubio","email":"","orcid":"","institution":"Cary Institute","correspondingAuthor":false,"prefix":"","firstName":"Vanessa","middleName":"","lastName":"Rubio","suffix":""}],"badges":[],"createdAt":"2024-12-02 20:31:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5566913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5566913/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-025-02696-1","type":"published","date":"2025-08-23T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78799599,"identity":"d95e8288-96ce-403e-93a3-ff22c1a86a05","added_by":"auto","created_at":"2025-03-19 06:16:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":298670,"visible":true,"origin":"","legend":"\u003cp\u003eRegularized canonical correlations of hyperspectral reflectance and gene expression for (a) \u003cem\u003eA. saccharum\u003c/em\u003e (left column) and (b) \u003cem\u003eA. rubrum \u003c/em\u003e(right column). Expressed genes with strong correlations (\u0026gt;0.7) along spectral reflectance (c and d) where the color of the lines represents annotated functional gene categories. The magnitude of loadings in different wavelengths (e and g) and the portion of strong canonical correlations (\u0026gt; 0.5) in different wavelength ranges (f and h).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5566913/v1/af888859ef3477180e6e5401.jpg"},{"id":89752517,"identity":"b63eddb7-41d6-4b67-906d-88435c66ef24","added_by":"auto","created_at":"2025-08-24 07:07:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":682130,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5566913/v1/dcc3ed69-ef2e-4d03-a9a6-4a045e25231b.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Linking Leaf Hyperspectral Reflectance and Gene Expression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eForests store\u0026thinsp;~\u0026thinsp;80% of the biomass and biodiversity on the planet and account for ~\u0026thinsp;75% of gross primary productivity on the terrestrial surface (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The structure, composition and health of these ecosystems are the net result of the successes and failures of individuals and species in a rapidly changing environmental context. Trees interface with their environment via their functional biology, often quantified via functional traits. Functional traits are relatively easy to measure traits representing where species fall along major tradeoff axes related to plant form and function and they offer a pragmatic approach for understanding how individuals to ecosystems interface with a changing world (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the widespread use of functional trait-based approaches in plant ecology, they have been, traditionally, limited in two ways (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). First, quantifying trait data across individuals and/or broad spatial extents is challenging despite their relative ease of measurement. That is, researchers often have to estimate the trait value of all conspecifics within or across populations using a species-level mean trait value and/or have only been able to quantify these traits on the scale of typical forest plot (e.g. 0.1\u0026ndash;0.5 km\u003csup\u003e2\u003c/sup\u003e) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Second, while the core set of functional traits typically measured in plant ecology does provide insights into key tradeoffs, they do not capture the breadth of the functional diversity within plants. That is, they may provide only topical insights into how trees, for example, are responding to key drivers of forest dynamics and health (e.g. drought, pest and pathogen outbreaks).\u003c/p\u003e \u003cp\u003eAdvances in remote sensing leveraging imaging spectroscopy are quickly removing the first limitation by providing maps of functional traits at broad spatial extents and fine spatial resolutions (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). For example, models of leaf traits based upon their reflectance have been applied to hyperspectral imagery for thousands of acres to entire countries to provide detailed predicted maps of leaf traits that are revolutionizing forest ecology (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Functional genomics holds the potential to overcome the second limitation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). For example, gene expression data provide broad and deep assays of plant functional diversity within and across individuals and species (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, the integration of functional genomics into forest ecology has lagged that of hyperspectral biology partly due to the cost of data acquisition and the associated challenge of quantifying gene expression on large numbers of individuals.\u003c/p\u003e \u003cp\u003eHere, we provide evidence that the hyperspectral reflectance of tree leaf tissue is strongly related to the expression of thousands of expressed genes in two ecologically dominant and economically important maple (\u003cem\u003eAcer\u003c/em\u003e) tree species from North America. The findings demonstrate the potential for spectral reflectance data to provide rapid and deep functional genomic assays on broad spatial extents that could transform forest ecology.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThe present study investigated the degree to which gene expression is linked to the spectral reflectance of leaf tissue as a means of potentially expanding the depth and breadth of plant function that can be inferred from hyperspectral imaging data. To this end, we used a regularized canonical correlation approach using leaf tissue from naturally occurring individuals from two of the most common and economically important tree species in eastern North America - sugar maple (\u003cem\u003eAcer saccharum\u003c/em\u003e [Sapindacae])(\u003cem\u003en\u0026thinsp;=\u0026thinsp;14\u003c/em\u003e) and red maple (\u003cem\u003eA. rubrum\u003c/em\u003e)(\u003cem\u003en\u0026thinsp;=\u0026thinsp;12\u003c/em\u003e). The analyses found evidence of strong correlations between the measured range of spectral reflectance (400\u0026ndash;2400 nm) and the expression of genes related to key ecological functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, when comparing the results across species, similar spectral ranges were the most likely to correlate with gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The analyses allowed us to investigate a broad range of expressed genes in the leaf tissue, but we were particularly interested in genes that were annotated with functions that are ecologically important and potentially challenging to assess using commonly measured functional traits. For example, we identified genes annotated to three key functions (plant-water relations, abscisic acid (ABA) response, and photosynthesis) that have strong canonical correlations (\u0026gt;\u0026thinsp;0.7) in the near-infrared (NIR) and shortwave infrared (SWIR) wavelengths (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Other pathogen, drought-specific and pigment related genes had moderately strong (0.5\u0026ndash;0.7) canonical correlations between their expression and reflectance (Fig.\u0026nbsp;3f, 3h). Importantly, genes identified in the same functional group and even in the same biological pathway may be differentially correlated with reflectance spectra owing to differences in the up and down regulation of genes. Further, while some wavelengths may have strong (\u0026gt;\u0026thinsp;0.7) canonical correlations with the expression of a gene, there are typically multiple additional wavelengths with moderate canonical correlations with expression. These moderate to strong canonical correlations from multiple wavelengths indicates that robust predictive models of gene expression from reflectance data can be generated using a larger dataset. Predictions of functional trait values from spectral data leverage trait-reflectance relationships from a broad range of wavelengths (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This finding demonstrates the capacity for leaf spectral reflectance data to predict the expression of functionally important genes. Next, we sought to investigate the relative ability of different spectral ranges to predict gene expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe three broad wavelength ranges (i.e. visible [VIS], NIR and SWIR) varied in the degree to which they correlated with gene expression. Despite the known utility of VIS wavelengths to predict phenotypic traits (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), our findings indicate that they have a reduced capacity to predict the expression of the genes studied. In both \u003cem\u003eA. saccharum\u003c/em\u003e and \u003cem\u003eA. rubrum\u003c/em\u003e, the highest magnitude loadings for reflectance in the regularized canonical correlation analysis were associated with the NIR wavelengths (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg). Specifically, NIR wavelengths can contribute to predictions of gene expression related to ABA, pathogens, photosynthesis, pigments and plant-water relations. Loadings from the SWIR range made both moderate and high contribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh). The SWIR wavelengths contributed to correlations of every category of gene function we annotated in our study including drought-specific genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh). The increased ability of SWIR, relative to NIR, to correlate with the expression of drought-specific genes is supported by previous studies that have noted the importance of both NIR and SWIR wavelengths for assessing the drought status of vegetation (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In summary, we find that NIR wavelengths tended to have relatively more linkages to the expression of genes, but the SWIR wavelengths have relatively stronger associations with some groups of genes and are likely critical additional information for producing robust predictions of gene expression that leverage both the NIR and SWIR wavelengths.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHere, we have shown that the expression of genes in leaf tissue across two tree species are associated with leaf-level spectral reflectance. This finding indicates that ecologists may be able to dramatically expand their capacity to generate functionally deep assays of function across broad spatial extents. However, there are some considerations going forward. First, roughly half of the genes in this study were correlated with spectral reflectance. Thus, this approach will not offer the ability to predict the expression of all genes or functions of interest to an ecologist. Second, predictive approaches like those used in the functional trait literature (e.g. partial least square regressions) are improved by large numbers of samples and, ideally, from individuals experiencing a broad range of environments. Lastly, we have focused only on the genes that can be reliably annotated or identified via gene ontologies. This results in a large number of genes that are not analyzed but are likely to have important functions. Despite these challenges, the potential for predicting the expression of hundreds to thousands of ecologically important genes from spectral reflectance is immense.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eLeaf tissue from naturally co-occurring adult canopy trees was collected at the University of Notre Dame Environmental Research Center. Leaf spectral reflectance data and mRNAseq from sun-exposed leaf tissue were collected using standard protocols and analyzed in unison using regularized canonical correlations. Further details are in SI Appendix.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData, Materials, and Software Availability\u003c/strong\u003e. All study data and data processing are included in the article and/or SI Appendix.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Biodiversity and Ecological Conservation Program at NASA (Grant No. 80NSSC22k1625). The authors are thankful for the Bernard J. Hank Family Endowment, which funds facilities, education and research at the University of Notre Dame Environmental Research Center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eNGS conceived of the research idea. YC, LM, VER, and NGS collected the data. YC, LM and NGS conducted the data analysis and wrote the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eC. Beer et al., Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e329\u003c/strong\u003e, 834-838 (2010)\u003c/li\u003e\n\u003cli\u003eY.D. Pan, R.A. Birdsey, O.L. Phillips, R.B. Jackson, The structure, distribution, and biomass of the world\u0026rsquo;s forests. \u003cem\u003eAnnu. Rev. Ecol. Evol. Syst.\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 593-622 (2013)\u003c/li\u003e\n\u003cli\u003eP.B. 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Spectral reflectance was measured on the adaxial leaf surface using an ASD FieldSpec 4 Hi-Res spectroradiometer and an attached ASD integrating sphere (Malvern Panalytical Ltd., Malvern, U.K.).\u0026nbsp;In accordance with the protocol provided in the ASD integrating sphere manual, we collected measurements of the reference standard between sample leaf reflectance measurements as well as measurements of stray light. During measurements, care was taken to avoid the midrib and other major venation. Measurements were taken from one healthy leaf per individual comparable to the leaf tissue frozen from the same branch. Five repeated spectral measurements were taken of the leaf and averaged for downstream analyses. Due to noise inherent in reflectance measurements at extreme wavelengths, the final data set was trimmed to only include reflectance values between 400 and 2400 nm.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eRNA Extraction, Sequencing and Bioinformatic Analysis:\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe frozen leaf tissue was stored in 10 ml cryovials in a -80\u0026deg;C freezer prior to RNA extraction. In the lab, tissue was hand ground into small pellets and transferred into a 2 ml tube with 2.8 mm ceramic beads to further grind the tissue power via a Qiagen TissueLysser II (Qiagen, Hilden, Germany). The totalRNA of each leaf sample was extracted using Qiagen RNeasy Plant Mini Kits via a Qiagen QIACube Connect using the standard kit protocols. The totalRNA samples were sent to Novogene (Davis, California) for sequencing using an Illumina NovaSeq X platform. The mRNA sequencing read data are paired-end 150bp with ~4 GB per sample. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sequencing reads were trimmed with fastp (1), and measured quality before and after data trimming using fastqc (2) and multiqc (3). We mapped trimmed reads to a published chromosome-scale \u003cem\u003eA. saccharum\u003c/em\u003e genome (4) using hisat2 (5) and summarized reads coverage with samtools (6) and stringtie (7). We also leveraged the existing annotation associated with the published \u003cem\u003eA. saccharum\u003c/em\u003e genome and annotated unknown genes with blastx (8), blastp (8), hmmscan (9). All gene and transcript annotations were wrapped together with Trinotate (10). Gene expression levels of each sample were summarized using transcript-level expression. It is important to note that we analyzed gene-level expression via summarized transcript-level expression, which may lead to \u0026ldquo;cancelation effects\u0026rdquo; in the RNAseq data.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eData Integration:\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eGene expression data for all samples were summarized into a single data matrix per species. In this analysis, we only analyzed genes with annotations that contained the words: ABA, drought, pathogen, photosynthesis, pigment or water for \u003cem\u003eA. saccharum\u0026nbsp;\u003c/em\u003e(\u003cem\u003en = 590\u003c/em\u003e) and \u003cem\u003eA. rubrum\u0026nbsp;\u003c/em\u003e(\u003cem\u003en = 1219\u003c/em\u003e). We focused on these general terms as they are linked to functions and processes of interest to forest ecologists. We note that other annotated or unannotated genes are ecologically important and could or should be analyzed by ecologists when they fully employ the approach presented in this report. Spectral reflectance measurements of all samples were summarized as a data matrix. To quantify the maximum correlation between the two groups of variables, we implemented a regularized canonical correlation analysis via the mixOmics package (11) in R (12). We optimized the regulatory parameters using a parallel version of the \u003cem\u003etune.rcc()\u003c/em\u003e function. Next, we calculated the loadings of each variable in each analysis under the optimal lamda value calculated. Last, we calculated and plotted the canonical correlation of each variable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eData Deposition:\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eSequencing reads used in this project were uploaded to BioProject (ID PRJNA1183736) in NCBI short reads archive. Leaf reflectance data and data analysis scripts were stored in a public Github repository (chenyanniii/spectral_trans).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSI References\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp;\u0026nbsp;S. Chen, Y. Zhou, Y. Chen, J. Gu, fastp: an ultra-fast all-in-one FASTQ preprocessor. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, i884-i890 (2018).\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp;\u0026nbsp;S. Andrews, Others, FastQC: a quality control tool for high throughput sequence data. [Preprint] (2010).\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp;\u0026nbsp;P. Ewels, M. Magnusson, S. Lundin, M. K\u0026auml;ller, MultiQC: summarize analysis results for multiple tools and samples in a single report. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 3047-3048 (2016).\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp;\u0026nbsp;S. L. 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Welham, \u003cem\u003eMultivariate data integration using R: Methods and applications with the mixOmics package\u003c/em\u003e (Chapman and Hall/CRC, 2021).\u003c/p\u003e\n\u003cp\u003e12. R Core Team, R: A Language and Environment for Statistical Computing. (2024).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"spectroscopy, transcriptomics, forest ecology, functional traits, functional ecology","lastPublishedDoi":"10.21203/rs.3.rs-5566913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5566913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHyperspectral reflectance data are utilized in ecology to predict functional trait values, but the diversity of functions captured by these traits is limited. Here, we demonstrate a novel integration of reflectance and to gene expression data for processes of interest to ecologists. We show linkages between the expression of ecologically important genes and reflectance data and the potential to transform the depth at which ecologists can rapidly estimate functional diversity.\u003c/p\u003e","manuscriptTitle":"Linking Leaf Hyperspectral Reflectance and Gene Expression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-19 06:16:23","doi":"10.21203/rs.3.rs-5566913/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f5dbb03b-3a35-484e-806b-b25e73302e07","owner":[],"postedDate":"March 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41161355,"name":"Biological sciences/Ecology/Forest ecology"},{"id":41161356,"name":"Earth and environmental sciences/Ecology/Forest ecology"}],"tags":[],"updatedAt":"2025-08-24T07:07:23+00:00","versionOfRecord":{"articleIdentity":"rs-5566913","link":"https://doi.org/10.1038/s43247-025-02696-1","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2025-08-23 04:00:00","publishedOnDateReadable":"August 23rd, 2025"},"versionCreatedAt":"2025-03-19 06:16:23","video":"","vorDoi":"10.1038/s43247-025-02696-1","vorDoiUrl":"https://doi.org/10.1038/s43247-025-02696-1","workflowStages":[]},"version":"v1","identity":"rs-5566913","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5566913","identity":"rs-5566913","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}