High-throughput Raman-activated cell sorting of microalgal genome-wide edited library revealed a new regulatory pathway for carotenoid synthesis

High-throughput Raman-activated cell sorting of microalgal genome-wide edited library revealed a new regulatory pathway for carotenoid synthesis | 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 Article High-throughput Raman-activated cell sorting of microalgal genome-wide edited library revealed a new regulatory pathway for carotenoid synthesis Jian Xu, Qintao Wang, Yanhai Gong, Lianhong Wang, Nana Lv, Xuefeng Du, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7026720/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Functional genomics has been hampered by the paucity of efficient methods that connect genotype and metabolic phenotype at single-cell resolution. Using the industrial microalga Nannochloropsis oceanica as a model, we introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated Cell Sorting (RACS). The CRISPR/Cas-generated library consists of 3,567 microalgal mutants derived from 2,397 effective guide RNAs. Label-free sorting of the library for high carotenoid content by RACS unravels mutations in the violaxanthin de-epoxidase ( noVDE ) or in the proteasome assembly chaperone 4 ( noPAC4 ) genes. Knocking out all five known noVDE s reveal that the high carotenoid content is due to violaxanthin increase, whilst noPAC4 knockout boosted carotenoid content with elevations in violaxanthin, zeaxanthin, and β-carotene. Genetic and transcriptomic evidences suggest two previously unknown modes of carotenogenesis regulation mediated by noPAC4: epigenetic mechanisms via histone deacetylase (HDAC) and post-translational controls by the 26S proteasome. Therefore, by label-freely sorting single-cell metabolic phenotype and rapidly yet unambiguously tracing it to a genotype, this new forward-genetics approach can greatly accelerate the discovery of new genes and pathways. Biological sciences/Genetics/Functional genomics/Mutagenesis Biological sciences/Molecular biology/Transcriptomics Biological sciences/Biological techniques/Genetic engineering Biological sciences/Biological techniques/High-throughput screening Genome-wide mutant library Raman-activated Cell Sorting carotenoids Nannochloropsis spp. functional gene discovery Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Microalgae are unicellular photosynthetic organisms that generate over 45% of the planet’s annual net primary biomass from carbon dioxide and sunlight 1 . Moreover, they harbor diverse metabolic capabilities for synthesis of valuable compounds such as proteins, lipids, carbohydrates, vitamins, antioxidants, polyunsaturated fatty acids (PUFAs), and pigments 2 , 3 . Therefore, microalgae are promising cell factories for carbon-neutral production of nutraceuticals, pharmaceuticals and biofuels 4 – 6 . To fully harness their metabolic capabilities, hundreds of microalgal genomes have been sequenced 7 ; yet, one striking finding is the large number of functionally unknown genes. For example, in the model oleaginous microalga of N. oceanica , over 50% of all predicted genes lack functional annotations, due to a lack of sufficient sequence similarity to other annotated genes 8 , 9 . Moreover, even in the case of high sequence similarity to genes with experimentally validated functions, direct transfer of functional annotation in the absence of experimental verification is often problematic, since distinguishing the functions among homologous genes is difficult 10 – 14 . Such lack of functional validation for a vast fraction of genes has long hindered mechanistic understanding and metabolic engineering of microalgae. To identify genes that underlie a particular metabolic trait, forward genetics approaches generally start with mutagenesis, whereby mutations are induced in the genomes, followed by screening for specific phenotypic changes due to the mutations, and then mapping of the genes responsible for the observed phenotype. Such approaches have been widely applied in microalgae to identify genes implicated in lipid metabolism and photosynthesis 15 , 16 , however limitations are profound. ( i ) Mutant library construction has traditionally relied on genetic mutagenesis induced by physical or chemical mutagens, or via random insertion 15 , and identifying genotypes with the desired traits is usually extremely laborious and time-consuming 17 – 19 . Moreover, mutants in so-generated mutant libraries generally have multiple mutation sites, which impedes an unambiguous assignment of mutant phenotype to a specific genetic locus. Whilst random insertion can yield single-locus mutations, many of these mutations may occur in intergenic or non-coding sequences, which may generate very subtle phenotypes that are difficult to measure 17 . ( ii ) Metabolic-phenotype-based screening poses a major challenge for microalgae. Whilst fluorescence-activated cell sorting (FACS) offers a valuable tool to address this challenge, its reliance on tedious fluorescent labeling for a specific protein or metabolite has hindered broad application and may introduce biases due to alteration of the natural state of cells 20 , 21 . Therefore, to increase throughput and reliability of forward genetics screens, label-free, high-throughput methods for sorting metabolic traits are urgently needed. Nannochloropsis are unicellular algae known for their rapid growth, adaptability to various environmental conditions, high lipid content, and production of high-value metabolites such as triacylglycerols (TAG), eicosapentaenoic acid (EPA), and carotenoids 22 . Employing the industrial oleaginous microalga of N. oceanica as a model, we introduced a new forward genetics platform that consists of a genome-wide single-gene-edited mutant library and high-throughput Raman-activated Cell Sorting (RACS). By generating the first such library for microalgae and then sorting it via RACS, we rapidly uncovered dozens of genes whose mutations reduced carotenoid contents, despite the lack of previous evidence linking them to carotenogenesis. In particular, two previously unknown modes of carotenogenesis regulation mediated by proteasome assembly chaperone 4 ( noPAC4 ) were revealed, which involves epigenetic mechanisms via histone deacetylase (HDAC) and post-translational controls by the 26S proteasome. By tracing single-cell metabolic phenotype to single-gene genotype in a label-free, high-throughput manner, the platform can greatly accelerate the discovery of new functional genes and pathways in microalgae and other organisms. Results Functional gene mining by sorting a single-gene-mutant library via single-cell metabolic phenotypes The classical approaches to construct a genetic mutant library rely on either random insertion (e.g., using Tn5 23 ) or application of physical or chemical mutagens 16 . To screen for a metabolic phenotype (e.g., content of metabolites like lipids 18 , 20 ), a mutant library is usually analyzed based on colonies grown on plate by mass spectroscopy or via biochemically/genetically introduced fluorescence probes by FACS ( Fig. 1A ). In contrast, we propose a strategy that features the creation of targeted, genome-wide single-gene-mutated strains by programmable gene editing, label-free sorting of single-cell metabolic phenotypes by RACS, and then establishing the single-cell genotype-phenotype links by sequencing the sorted cells ( Fig. 1B ). The platform consists of two main modules. ( i ) A genome-wide single-gene targeted mutagenesis library for N. oceanica was created by the CRISPR/Cas system, which greatly simplifies large-scale single-gene editing (and thus mutagenesis) due to its targeted-mutagenesis nature, as well as its efficiency, simplicity, and programmability 24 . Moreover, mutation sites can be tracked using the gRNA sequence, which is easily detectable. No such libraries have previously been reported for microalgae 25 , despite the successes in various higher plants such as rice, tomato, soybean, and maize 26 . ( ii ) A metabolism-driven label-free single-cell screening platform was established for N. oceanica based on high-throughput RACS. A spontaneous single-cell Raman spectrum (SCRS) provides the intrinsic biochemical profile of a cell at a given state and thus can be considered as a function-based snapshot of the molecular state of the cell. Importantly, this strategy is label-free, culture-free, noninvasive, rich in metabolic information, and broadly applicable 27 . For instance, the FlowRACS system that we developed for high-throughput RACS can profile and sort fungal and human cells based on SCRS 28 – 30 . Construction of a CRISPR/Cas system-based genome-wide mutant library of N. oceanica To generate a CRISPR/Cas-based mutant library of N. oceanica ( Fig. 2A ; Methods ), a gRNA library was designed, synthesized and subsequently ligated to plasmids containing the CRISPR/Cas system. This plasmid library was transformed into N. oceanica to construct the mutant library, which was then sequenced using next-generation sequencing (NGS) to identify the mutation sites. Precise generation of gRNAs was ensured by engineering them with hammerhead (HH) self-cleaving ribozymes at the 5’ end and hepatitis delta virus (HDV) self-cleaving ribozymes at the 3’ end in the episome-based CRISPR/Cas system in N. oceanica 31 . The HH structure dictates that its first six bases must correspond to the reverse complement of the first six bases of the gRNA sequence ( Fig. S1 A , purple). After the ligation of the gRNA target sequence library to the plasmid containing the CRISPR/Cas system via infusion, we sequenced 15 colonies and identified just one accurately ligated plasmid ( Fig. S1 A , green): such observed low accuracy impeded the knockout efficiency in constructing the mutant library. It seemed that the loop structure necessary for HH self-assembly obstructs the correct ligation of the gRNA PCR library to the plasmid that harbors the CRISPR/Cas system. To streamline the design and ligation of the gRNA pool to the plasmid and to ensure accurate gRNA expression, we employed tRNA from glycine in N. oceanica instead of HH self-cleaving ribozymes ( Fig. S1 B ; Table S1 ). To evaluate the knock-out efficiency, a light-harvesting complex protein (LHC; NO13G02860) was chosen as the target gene. Sequencing of the target sites in twelve transformants revealed mutations in each of them ( Fig. S1 B ), indicating a high-efficiency of tRNA-based CRISPR/Cas system in N. oceanica . Moreover, the accuracy of gRNA target sequence ligation to the system is 85%, as 17 among the 20 sequenced colonies were correct ( Fig. S1 C , blue). Next, we assessed the coverage, uniformity, and accuracy of the plasmid library which underlie the quality of the eventual tRNA-based CRISPR/Cas mutant library. These factors depend on the design, synthesis, and ligation (to the CRISPR/Cas-encoded plasmid) of the gRNA library. To maximize the coverage, a total of 9,709 gRNAs were designed to span ~ 94% of the entire protein-coding sequences ( Supplemental Data 1 ; Methods ). The gRNA target sites from both libraries were amplified ( Table S1 ) and sequenced ( Fig. 2B ), indicating 92% accuracy of the gRNAs in the gRNA PCR library, and 84% in the plasmid library ( Fig. 2C ). Furthermore, in the plasmid library, all designed gRNAs were represented ( Fig. 2C ), with a high degree of uniformity in the distribution of gRNA abundance (Gini index of 0.04 for the plasmid library and 0.02 for the gRNA PCR library; Fig. 2D ), thus the library is well-balanced and each gRNA is represented at a similar level. These findings confirm the high quality of the plasmid library for a CRISPR/Cas system-based genome-wide single-gene mutated library of N. oceanica . High mutational efficiency and low bias in mutated genes in the N. oceanica mutant library After the transformation, a total of 5,760 transformants were distributed among 240 24-well plates, and mutation sites in each well was identified via NGS ( Fig. 3 ). Specifically, the 24 transformants from each 24-well plate were pooled for whole genome sequencing to map the mutations present within these genomes ( Fig. 3A ). However, the existence of natural mutations in the genome and sequencing errors posed challenges in accurate detection of mutation sites. To tackle this issue, the gRNA from each well was examined to distinguish the CRISPR/Cas-induced mutation sites. Microalgal transformants in the 240 24-well plates were organized into ten pools, with each pool (24 plates) carefully collected ( Fig. 3B ). To confirm the gRNA composition within each well, the gRNAs were amplified using PCR (primers in Table S1 ) and sequenced by NGS. Then the gRNAs in each well were verified based on their probability of occurrence. By aligning the gRNAs against the genome sequence at the designated target sites in each well, we found that 2,851 of the 5,760 colonies exhibited exactly one mutated gene, 716 colonies displayed two or more mutated genes, and 2,193 colonies showed no mutated genes ( Supplemental Data 2 ; Fig. 3C ). Collectively, 2,397 effective gRNAs were identified ( Methods ; https://nandesyn.single-cell.cn/CRISPR_library ), indicating 41.6% efficiency for obtaining valid gRNAs among the total mutants (5,760 mutants, Fig. 3D ). The mutations were categorized into nine distinct classes: 3,663 frameshift-variants, 214 missense-variants, 115 disruptive-inframe-deletions, 78 synonymous-variants, 54 conservative-inframe-deletions, 27 disruptive-inframe-insertions, 13 conservative-inframe-insertions, 10 stop-gained, and 117 variants of multiple types ( Fig. 3D ). A substantial majority of the mutations (85.4%) were classified as frameshift-variants, which significantly contribute to the generation of loss-of-function mutants for reverse genetics in N. oceanica. These results validate the successful construction of a CRISPR/Cas-based N. oceanica mutant library, providing 3,567 mutated colonies and 2,397 efficient gRNAs for subsequent genome editing and mutant screening endeavors. Quantitative analyses of the library revealed 78.1% of its mutated genes as unique among the mutant strains ( Fig. 4A ), slightly lower than the theoretically expected probability of 84%. This discrepancy may be attributed to the presence of lethal genes that cannot yield viable mutants. The average proximity between adjacent mutated genes is 12.5 ± 12.1 kb, showing no significant deviation from random sampling (Wilcoxon test, p > 0.05; Fig. 4B ). Based on the Clusters of Orthologous Genes (COG) database, only the COG category of “translation, ribosomal structure, and biogenesis” exhibits significant difference (in this case, lower-than-expected representation) from the target proteome (Chi-square test, adjusted p < 0.01; Fig. 4C ), which may be due to a higher prevalence of lethal genes within this category. Overall, the mutant library exhibits high quality, characterized by a uniform distribution of mutated genes that is genome wide and among the functional categories of proteome. Furthermore, to evaluate the effect of gRNA base preference on editing efficacy in N. oceanica , we investigated the nucleotide distribution at different positions within the designed and effective gRNAs ( Fig. S2 ). No discernible preference for specific nucleotides was found at any position when compared with the designed and effective gRNAs (Z-test, p > 0.05; Fig. 4D ), indicating the efficient design of gRNA library. Given the high uniformity of the mutant library, we observed significant positive Pearson correlation between the number of mutated genes and the number of transformants ( Fig. 4E ), suggesting a saturated mutant library can be constructed by collecting additional mutant clones. Thus, a high-quality genome-wide knockout mutant library of N. oceanica was constructed with high efficiency and high uniformity. High-throughput SCRS-based sorting identified mutated genes underlying carotenoid overproduction Carotenoids, a class of terpenoid pigments with C40 backbones found in microalgae and plants, are pivotal to photosynthesis and widely used in many industries due to their health benefits 32 . Traditional screening for carotenogenesis genes typically involves time-consuming, labor-intensive, and low-throughput culturing of mutants and measuring changes in carotenoid levels from bulk biomass, before identifying target genes ( Fig. 5A ) 33 . In contrast, we propose that SCRS-based sorting of the genome-wide knockout mutant library enables single-cell-resolved, high-throughput screening of mutants with high carotenoid content, thus greatly accelerating function-directed gene mining ( Fig. 5A ). Carotenoids exhibit characteristic resonance Raman peaks near ~ 1520 cm − 1 , ~ 1156 cm − 1 , and ~ 1006 cm − 1 , whereas chlorophyll shows no peaks in these regions 34 , 35 , thus intracellular carotenoids can be detected without interference from chlorophyll signals in photosynthetic cells such as Chlamydomonas reinhardtii and Haematococcus pluvialis 29 , 34 , 36 , 37 . In N. oceanica , zeaxanthin, violaxanthin, and β-carotene are the three major carotenoids, collectively accounting for approx.75% of the total carotenoids 38 . Raman spectra of a gradient of these individual purified carotenoids revealed the concentrations of β-carotene, violaxanthin, and zeaxanthin are linearly dependent ( R = 0.99, 1, and 1, respectively) on normalized intensities at 1511 cm − 1 ( I 1511 - I 1800 ), 1522 cm − 1 ( I 1522 - I 1800 ), and 1517 cm − 1 ( I 1517 - I 1800 ; 1800 cm − 1 used as baseline due to the absence of biological molecules vibrations from 1800 to 1840 cm − 1 ; Fig. 5B-D ). We then profiled the SCRS of the N. oceanica cells from the CRISPR/Cas mutant library (after five-day cultured), with the 532 nm laser and throughput of 600 cells min − 1 in FlowRACS ( Fig. 5A ). The microalgal SCRS showed three most prominent bands at 1509 cm − 1 , 1154 cm − 1 and 996 cm − 1 ( Fig. 5E ), which agree with the carotenoid standards and support using these peaks for carotenoid quantification in N. oceanica . Among these peaks, 1509 cm − 1 (C = C bond stretching vibrations in carotenoids) showed the highest intensity, thus the “ I 1,509 - I 1,800 > x ” criterion was applied to sort carotenoid-overproducing cells from the mutant library. To determine the sorting threshold of “ x ”, over 1,000 cells were first measured in a single FlowRACS run, while the value of I 1,509 - I 1,800 in SCRS that separates the top 1% of cells in intracellular carotenoid content from the remaining 99% of cells was chosen as the “ x ”. Given that direct detection of target sites was challenging due to natural variations and sequencing errors, we relied on gRNAs as markers for identifying mutated genes. Considering a throughput of ~ 600 cells min − 1 while retaining only the top 1% of cells, the sorted cells during five hours of screening were subjected to DNA amplification by Multiple Displacement Amplification (MDA) with primers targeting the plasmid within the CRISPR/Cas system ( Table S1 ; Methods ), then the MDA products were amplified by PCR and sequenced by NGS to analyze the gRNA abundance. In addition, the gRNA sequences in the unsorted mutant library were amplified by PCR and sequenced by NGS as a control. The screening experiment is divided into two steps. First, to determine whether there are mutant strains with the target phenotype in the mutant library, over 10,000 microalgal cells from the library and a wild-type sample (WT) were subjected to FlowRACS. The SCRS obtained from the mutant library exhibited significantly higher heterogeneity than the WT, as evidenced by the broader variations in Raman intensities across different Raman shifts ( Fig. 5E-F ). These variations encompassed, but were not limited to, characteristic carotenoid Raman peaks ( v 1: ~996 cm − 1 ; v 2: ~1,154 cm − 1 ; v 3: ~1,509 cm − 1 ). Indeed, the standard deviations of Raman intensities at each Raman shift were on average 1.69-fold higher in the mutant library than in the WT (Wilcoxon test, p < 0.0001; Fig. 5F ). Moreover, the v 3 peak intensities in the mutant library were averagely 1.61-fold higher than in the WT (Wilcoxon test, p < 0.0001; Fig. 5G ), suggesting that the increase in carotenoid content in at least some of the cells in the mutant library. Notably, following subsampling of 4,000 cells for both the mutant library and the WT sample, the screening identified 26 mutants with intensity exceeding 6,000 at 1,509 cm − 1 , whereas only four cells in the WT sample exhibited similarly high Raman intensities at the v3 peak ( Fig. 5H ). These results suggested the presence of many carotenoid-overproducing mutants in the library, and supported the suitability of FlowRACS for screening such metabolic phenotypes in microalgae. Second, we sorted carotenoid-overproducing mutants through three independent FlowRACS runs based on v 3 peak intensities. Based on the fold-change of gRNA abundances before- and post-sorting ( Methods ), 40, 44, and 56 genes were found enriched respectively ( Supplemental data 3 ). These mutated genes are related to over 20 pathways including carotenoid biosynthesis (e. g., noVDEs ) and photosynthesis ( Fig. 5I ; Supplemental data 3 ), suggesting a direct or pleiotropic link between carotenoid biosynthesis and other metabolic pathways. Moreover, among the collectively 109 unique mutated genes ( Supplemental data 3 ), 78 are of unknown function, nine of which were consistently identified across all three independent runs, while 13 were detected in two of the three runs ( Fig. 5I-J ). Most of the shared genes seem not directly linked to carotenoid biosynthesis, with eight of them classified as unknown genes, i.e., suggesting novel mechanisms. Among the genes shared by at least two of the three FlowRACS runs are two violaxanthin de-epoxidases ( VDE s), which may be responsible for the de-epoxidation of violaxanthin within the thylakoid membrane in the carotenoid biosynthesis pathway. Specifically, in higher plants, VDE is activated under high light and converts violaxanthin to antheraxanthin and zeaxanthin, resulting in a decrease in violaxanthin content 39 ; thus VDE knock-out may result in the reduction of violaxanthin (and thus of carotenoids). In N. oceanica , violaxanthin content can be regulated by carotenoid biosynthetic genes such as carotenoid isomerase (from Phaeodactylum tricornutum 40 ) and zeaxanthin epoxidase ( ZEP ) 41 , yet functional roles of the noVDE s are still unclear. Moreover, many of the genes represent entirely unexpected links to carotenoid metabolism. For instance, NO02G03540, the proteasome assembly chaperone 4 ( PAC4 ), was among those with the highest enrichment (with log-fold changes of 16.8, 6.8, and 16.4 in the three replicates; Fig. 5I ). This is a protein that facilitates biogenesis of the 26S proteasome 42 , a large protein complex responsible for degrading ubiquitin-dependent proteins in eukaryotic cells 43 . PAC4 and other proteasome assembly chaperones are known as potential targets for inhibiting accurate proteasome assembly in cancer cells 44 , 45 . In plants, chloroplast-targeted preproteins may undergo ubiquitin-dependent degradation by the 26S proteasome to prevent their accumulation in the cytosol and help shaping the chloroplast proteome 46 . However, no evidence has ever implicated PAC4 in pigment metabolism. Collectively, direct phenotypic screening of the N. oceanica genome-wide targeted mutant library by FlowRACS rapidly uncovered many unexpected genes associated with elevation in single-cell carotenoid content. To dissect the underlying mechanisms, we selected VDE s and PAC4 as targets for validating in vivo activities via metabolic engineering. VDEs contribute to accumulation of carotenoids in N. oceanica To probe the in vivo function of noVDE s, we started from the time-series transcriptomes of N. oceanica under six conditions, including: very low carbon levels (VLC, 100 ppm CO 2 ), high carbon levels (HC, 5% CO 2 ), nitrogen-depleted (N-), nitrogen-replete (N+), exposure to red or blue light ( Fig. S3A ; Table S2 ; https://nandesyn.single-cell.cn ). Under VLC, noVDE1-5 transcript levels ( noVDE1 : NO24G00840, noVDE2 : NO20G02350, noVDE3 : NO11G00920, noVDE4 : NO03G00410, noVDE5 : NO08G01830) are 30.9%, 7.5%, 33.7%, 62.3%, and 136% higher than HC, respectively. This may be attributed to insufficient carbon supply for the dark reactions of photosynthesis under VLC. The excess absorption of light by photosynthetic pigments, relative to the cellular metabolic capacity, drives the overreduction of the photosynthetic electron transport chain, resulting in the generation of reactive oxygen species (ROS) 47 . Thus, the activation of noVDE s can facilitate the conversion of violaxanthin to zeaxanthin, which enhances non-photochemical quenching (NPQ) and aids in the scavenging of chlorophyll triplets and ROS 48 . These results support the roles of noVDE s in carotenoid biosynthesis in N. oceanica . To avoid compensatory effects of genes with similar function, a tRNA-based CRISPR/Cas system was employed to simultaneously mutate all predicted noVDE s using a single plasmid, resulting in two mutants, termed novde-1 and novde-2 ( Fig. 6A ), each of which carries frameshift mutations in all five noVDE s (except noVDE4 in novde-1 which carries a 18-base deletion). These mutants showed significant alterations in photosynthetic performance, plus in the levels of the violaxanthin and β-carotene ( Fig. 6B-D ). Under low light conditions, the Fv/Fm ratios for novde-1 and novde-2 increased by 4.0% and 3.4%, respectively, compared to the WT ( Fig. 6B ). In addition, the violaxanthin levels were increased by 43.3% and 24.8% ( Fig. 6C ), while β-carotene levels increased by 29.8% and 30.9% ( Fig. 6D ), respectively. Furthermore, the total content of violaxanthin, β-carotene and zeaxanthin increased by 21.7% and 11.4%, respectively ( Fig. 6F ). Under high light, compared to the WT, the Fv/Fm ratios in novde-1 and novde-2 increased by 8.3% and 8.1%, respectively ( Fig. 6B ), while the β-carotene levels increased by 35.6% and 34.2%, respectively ( Fig. 6C ). Moreover, the changes were not accompanied by significant alterations in growth rate, biomass, chlorophyll a , or zeaxanthin content, with the exception that the biomass of novde-2 decreased by 7.8% ( Fig. S4A-C ; Fig. 6E ). These findings validate the role of noVDE s in converting violaxanthin to zeaxanthin, and explain the accumulation of violaxanthin and higher carotenoid contents in noVDE s-knockout mutants. Discovery of a novel role of PAC4 that enhances carotenoid synthesis in N. oceanica NO02G03540 encodes noPAC4, putatively a proteasome assembly chaperone involved in assembly of the 26S proteasome. noPAC4 shows 37% sequence identity to human PAC4 (NCBI Reference Sequence: NP_001129222.1), yet no significant similarity to yeast PBA4 (GenBank: DAA11291.1). Given the lack of evidence linking noPAC4 to carotenoid biosynthesis in any organisms, we analyzed the rich N. oceanica transcriptomes collected under diverse conditions in the NanDeSyn database 8 , 9 , to probe mechanisms of the high-carotenoid phenotype in noPAC4 -edited mutants ( Methods ). Under HC and VLC conditions, at 24 hours, noPAC4 expression is 51% lower under VLC than HC ( Fig. S3A ), while ~ 75% of the 52 genes underlying 26S proteasome were downregulated ( Fig. S3B ). Notably, the noPAC4 transcript shows a strong positive correlation (median Pearson’s r = 0.88) with those of 26S proteasome genes over 3, 6, 12, and 24 hours ( Fig. S3B ), suggesting functional connections between noPAC4 regulation, 26S proteasome activity and carotenoid biosynthesis. We propose that enhanced protein biosynthesis under HC conditions raises 26S proteasome activity, which then alters carotenoid biosynthesis. Phytoene synthase ( PSY ) catalyzes carotenogenesis by condensing two molecules of geranylgeranyl diphosphate (GGPP) to produce the C40 hydrocarbon 15-cis-phytoene. Acting as the first committed step in carotenogenesis, PSY directs the metabolic flux into the carotenoid biosynthetic pathway. The precursor protein of PSY can be ubiquitinated by the plastid protein-sensing ring E3 ligase 1 ( PPSR1 ) and subsequently degraded by the 26S proteasome 49 . We hypothesize that the knockout of noPAC4 decreases the efficacy of the 26S proteasome, resulting in an increased transport of precursor proteins of PSY into the chloroplast, which in turn leads to enhanced carotenoid biosynthesis. To test these hypotheses, we independently knocked out noPAC4 ( Fig. 7A ). The mutants of nopac4-1 and nopac4-2 exhibit significant changes in growth rate, photosynthesis, and carotenoid content ( Fig. 7B-F, S4D-E ). Specifically, under low light (50 µmol photons m − 2 s − 1 ) conditions, growth rate increased by 4.3% and 8.6%, respectively, compared to WT, while biomass decreased by 13.5% and 9.9%, respectively ( Fig. S4D-E ). The Fv/Fm ratios for nopac4-1 and nopac4-2 increased by 5.2% and 6.1%, respectively ( Fig. 7B ). Moreover, violaxanthin levels increased by 15.6% and 12.3% ( Fig. 7C ); β-carotene levels increased by 18.7% and 30.2% ( Fig. 7D ); and zeaxanthin levels increased by 16.6% and 22.1% ( Fig. 7E ), respectively. Furthermore, the total content of violaxanthin, β-carotene and zeaxanthin increased by 19.6% and 19.5%, respectively ( Fig. 7F ). However, under high light (300 µmol photons m − 2 s − 1 ), no such difference was observed between WT and mutants, except for the 8.4% and 7.6% reduction in zeaxanthin levels in the mutants ( Fig. 7B-E ). The chlorophyll a content was unchanged ( Fig. S4F ), suggesting that the SCRS-based mutant screening was specifically based on carotenoid accumulation yet independent of chlorophyll fluctuations. Therefore, the substantial increase in carotenoid levels in noPAC4 -knockout mutants under low light is consistent with the single-cell Raman-based phenotypes as identified and sorted. Moreover, the distinct physiological responses to noPAC4 knock-out from noVDEs knock-outs suggest FlowRACS can identify mutants with very different mechanisms that underlie a target phenotype. Furthermore, we profiled the transcriptomes of noPAC4- knockout mutants and WT under low light via RNA-Seq (in triplicates; Fig. S5 ). In the mutants ( Supplemental data 4 ), 73 differentially expressed genes (DEGs) were identified compared to the WT. For example, a histone deacetylase ( HDAC , NO11G01600) is much lower in transcript level than the WT, by 43.0% and 54.8% in nopac4-1 and nopac4-2 , respectively ( Fig. 7G ). HDAC interference accelerates the ripening process and increases β-carotene accumulation in tomato 50 , raising the possibility that the reduced expression of HDAC may also promote carotenoid biosynthesis in N. oceanica . In addition, transcripts of retinol dehydrogenase ( RDH , NO04G04240) are 64.5% and 64.4% lower than that of WT, respectively ( Fig. 7G ). RDH β-carotene 15,15’-dioxygenase ( BCO ) cleaves β-carotene into retinal, which is then reduced to retinol by the retinol dehydrogenase 51 . The decreased expression of RDH may reduce the cleavage of β-carotene, resulting in increased carotenoid accumulation. In addition, the expression of proteasome-associated genes was up-regulated in both nopac4-1 and nopac4-2 , as indicated by the KEGG gene set enrichment analysis ( Fig. 7H-I and Fig. S6 ; Methods ), suggesting noPAC4-mediated regulation of 26S proteasome activity. Collectively, these results support the attenuation of 26S proteasome function by noPAC4 knockout, which may enhance the import of precursor proteins (e.g., PSY) into the chloroplast, thereby stimulating carotenoid biosynthesis. Discussion In N. oceanica , carotenoid biosynthesis starts with the condensation of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), derived from the methylerythritol phosphate (MEP) pathway, to form the 20-carbon geranylgeranyl diphosphate (GGPP). This reaction is catalyzed by GGPP synthase. Subsequently, GGPP is converted to the 40-carbon phytoene by the action of phytoene synthase. The conversion of phytoene to β-carotene involves a series of enzymatic steps, including desaturation, isomerization, and cyclization, mediated by phytoene desaturase (PDS), ζ-carotene isomerase, ζ-carotene desaturase, carotenoid isomerase, and lycopene β-cyclase (LCYB). Following the formation of β-carotene, hydroxylation reactions catalyzed by heme-containing cytochrome P450 enzymes lead to the synthesis of zeaxanthin. Zeaxanthin can be further modified to violaxanthin by zeaxanthin epoxidase (ZEP). Additionally, violaxanthin can be transformed into neoxanthin by violaxanthin de-epoxidase-like (VDL) and further metabolized to vaucheriaxanthin through enzymatic steps that remain to be fully elucidated. To date, only a few enzymes in the carotenoid biosynthesis pathway of N. oceanica have been characterized in vivo , including PDS , LCYB , ZEP , and VDL 38 , 41 , 52 , 53 . Here, by establishing a platform that comprises a genome-wide targeted knockout library and high-throughput RACS, we demonstrated an efficient strategy to mine functional genes by efficiently tracing a metabolic trait to its genotypes. From the CRISPR/Cas-generated library of 3,567 N. oceanica mutants derived from 2,397 effective guide RNAs, label-free sorting for high single-cell carotenoid contents via SCRS rapidly unraveled its underlying genes, including both expected (e.g., noVDEs ) and unexpected (e.g., noPAC4 ) ones, which were then validated. These allowed us to propose a multi-level regulation model that reveals new insights into carotenoid biosynthesis in this microalga ( Fig. 8 ). ( i ) At the transcriptional level, noPAC4 may reduce the expression of HDAC , thereby influencing transcript level of carotenoid-synthetic genes through epigenetic mechanisms. ( ii ) At the post-translational level, noPAC4 regulates the 26S proteasome, which in turn maintains chloroplast proteostasis 46 and influences degradation of carotenogenic enzymes such as PSY 49 , thereby affecting carotenoid accumulation. ( iii ) Carotenoid catabolic enzymes such as noVDEs convert the carotenoid violaxanthin into antheraxanthin and zeaxanthin, as evidenced by the noVDE -knockout induced 43.3% increase in violaxanthin content. Moreover, reducing retinol synthesis by inhibiting RDH can also raise carotenoid level. Studies of microalgal carotenoid metabolism have mainly focused on key enzymes such as PDS , LCYB , ZEP , and VDL 41 , 54 . Here we identified noVDEs as an important player, and showed that knockout of five noVDE genes in a single transformation resulted in 43.3% increase of violaxanthin content in N. oceanica , which provides an effective strategy for violaxanthin overproduction. For regulatory mechanisms, reactive oxygen species (ROS), nitric oxide (NO), and calcium ions (Ca 2+ ) were found as signaling molecules under salinity or light stresses 29 , 55 . In contrast, we revealed a post-translational mechanism that modulates carotenoid levels involving proteasome assembly control through PAC. These findings uncovered a new strategy for engineering carotenoid production, while extended the impact of the 26S proteasome-mediated protein degradation from protein misfolding-related disorders such as neurodegenerative diseases 56 to the unexpected realms of plant pigment synthesis and photosynthesis. These discoveries underscore the power of screening a genome-wide targeted knockout mutant library, which is not dependent on a priori knowledge or hypotheses about the underlying genotype. Notably, such mutant libraries have not been reported for any microalgae before, due to their relatively low transformation or genome-editing efficiency 57 . To tackle this challenge, we devised a tRNA-based CRISPR/Cas system for N. oceanica that simplifies the gRNA design and increases the accuracy of ligation for plasmid library construction. Compared to the hammerhead-based CRISPR/Cas system 31 , the tRNA-based system eliminates the complex design of the hammerhead structure as well as the spontaneous formation of the stem loop structure, thereby streamlining plasmid construction. This library offers key advantages over those generated by random insertion, chemical mutagens or physical mutagenesis 58 . ( i ) It enabled rapid and low-cost identification of mutation sites by employing gRNAs as markers to analyze target site mutations. ( ii ) It elevates the proportion of exon mutations: all gRNAs were targeted to such coding regions, in contrast to that only 40% of genes were of exon mutation in a random insertion based C. reinhardtii mutant library 15 . ( iii ) Its genome-wide targeted knockout feature allowed unambiguous and efficient mining of genotypes that underlie a target phenotype by gRNA enrichment, which however is particularly challenging for random mutagenesis libraries due to the presence of multiple mutation sites in a mutant genome 59 . In screening such large mutant libraries for intracellular metabolites, throughput has long been the primary bottleneck, because the classical yet time-consuming, tedious and costly strategy of agar plating, colony picking, culture for sufficient clonal biomass, biomass lysis and profiling cell extracts for metabolite content still dominates both large-scale bio-foundry facilities and small labs. This process is particularly painful for autotrophic eukaryotic microalgae due to their typically slower growth than bacterial and fungal chasses 15 , 17 . Moreover, many mutants may fail to proliferate during the culture, leading to the loss of crucial genes 19 , 20 . FACS can greatly accelerate this process by sorting individual cells 16 ,yet its application is limited by the narrow scope of metabolites that can be labeled, the interference of autofluorescence, and the often very demanding process of introducing the label either biochemically or genetically. For example, as the basic structural units of photosynthetic apparatus that harvest light for photosynthesis, quench the energy excess under high-light stress and prevent the formation of highly reactive singlet oxygen 60 , carotenoids cannot be directly screened by FACS due to the interference of chlorophyll fluorescence. In fact, current quantification methods for carotenoids including near-infrared reflectance spectroscopy (NIRS), visible spectrometry, laser photoacoustic spectroscopy (PAS), UV-visible spectrometry, high-performance liquid chromatography (HPLC) and mass spectrometry (MS) all require carotenoid extraction from a significant amount of bulk biomass 61 . To tackle this challenge, we established a microfluidics-based, label-free, non-destructive, high-throughput approach for quantifying and then sorting of single-cell carotenoid contents directly from mutant mixtures, based on RACS. We showed that a spontaneous SCRS can accurately quantify contents of total carotenoids at single-cell resolution. Moreover, such contents were sorted at a high throughput of ~ 600 cells min − 1 , so the top-performing 1% of cells can be genotyped for enriched gRNA sequences to trace the phenotype to underlying genotypes. For instance, noPAC4 mutants with increased violaxanthin, zeaxanthin, and β-carotene levels were obtained in just five hours of a FlowRACS run. By label-freely and non-destructively sorting individual cells for single-cell carotenoid content, the Raman cytometry avoids the tedious fluorescence labeling, saves the arduous time and efforts for agar plating, colony picking and biomass extraction, and minimizes the potential loss of slow-growing mutants during the various stages of cultivation. Besides carotenoids, a spontaneous SCRS can depict other high-value pigments (e.g., astaxanthin), lipids (e.g., TAG and DHA) and polysaccharides (e.g., starch), as well as their interconversion in the cell 27 – 30 . Moreover, key traits of cell factories, such as viability, vitality, assimilation rate of specific substrates, susceptibility and response to stresses can be profiled by inserting a stable-isotope probing step before SCRS acquisition and sorting 27 , 62 . Therefore, one direction is to take advantage of such rich and diverse single-cell metabolic phenome encoded in a SCRS for single-trait or multi-trait gene mining at a systematic fashion, from the genome-wide N. oceanica mutant library here or even directly from an environmental microbiome sample. On the other hand, due to signal overlaying of metabolites, SCRS is often limited by difficulty in distinguishing chemical species within a metabolite class or detecting low-level metabolites. To deconvolute SCRS, a RACS-coupled mass spectrometry workflow that supports machine learning of SCRS and the matching population-level metabolomes can be established. Collectively, by label-free sorting genome-wide targeted mutant libraries for single-cell metabolic features, such a forward-genetics approach would greatly accelerate the discovery of new genes and pathways, in not just microalgae but bacterial, fungal, plant and mammalian cells. Methods Strains and growth conditions The Nannochloropsis oceanica strains were cultivated in modified f/2 liquid medium 63 , which containing 35 g L – 1 sea salt, 1,000 mg L – 1 NaNO 3 , 66.6 mg L – 1 NaH 2 PO 4 •H 2 O, 3.65 mg L – 1 FeCl 3 •6H 2 O, 4.37 mg L – 1 Na 2 EDTA•2H 2 O, 0.0196 mg L – 1 CuSO 4 •5H 2 O, 0.0126 mg L – 1 Na 2 MoO 4 •2H 2 O, 0.044 mg L – 1 ZnSO 4 •7H 2 O, 0.0109 mg L – 1 CoCl 2 •6H 2 O, 0.036 mg L – 1 MnCl 2 •4H 2 O, 5 mg L – 1 VB 12 , 5 mg L – 1 biotin, and 0.1 mg L – 1 thiamine HCl. Continuous illumination (50 µmol photons m – 2 s – 1 ) at 25°C was applied. For high-light induction conditions (300 µmol photons m – 2 s – 1 ), the cells were harvested after a 5-day culture period by centrifugation (3,500 g for 5 min), and then resuspended in fresh f/2 medium to achieve a cell density of OD750 = 1. Cell growth was determined based on biomass dry weight and optical density (OD750). Genetic transformation of the wild-type N. oceanica CRISPR/Cas plasmids were introduced into N. oceanica cells as circular DNA by electroporation, as described 64 . Briefly, wild-type N. oceanica cells were cultured in 100 mL to the logarithmic growth phase and then harvested by centrifugation at 5000 g for 5 minutes at 4°C. The cells underwent two rounds of wash with 25 mL of 375 mM sorbitol at 4°C. For each transformation, 200 µL of cells were combined with 2 µg of the cassette in an electroporation cuvette with a 2 mm gap before electroporation. The electroporation was carried out using a BTX ECM 630 system with a 1,100 V cm − 1 field strength, 50 µF capacitance, and 600 Ohm shunt resistance. The cells were promptly mixed with 5 mL of f/2 medium and incubated under low light conditions at 120 rpm for 48 hours. Subsequently, the cells were plated on f/2 agar plates supplemented with 5 mg L − 1 zeocin and 1.6 g L − 1 NaHCO 3 . Colonies emerged around three weeks later and were typically transferred after 25 days. CRISPR/Cas library construction and genetic screening strategy All gRNA candidates for each protein-coding gene were predicted by a local version of CHOPCHOP. The parameters used were “-t CODING -T 1 -g 20 -BB AGGCTAGTCCGT -M NGG -P -J -3 'PRODUCT_SIZE_MIN = 250, PRODUCT_SIZE_MAX = 350, PRIMER_MIN_SIZE = 18, PRIMER_MAX_SIZE = 25, PRIMER_OPT_SIZE = 22, PRIMER_MIN_TM = 57, PRIMER_MAX_TM = 63, PRIMER_OPT_TM = 60' -a 60 -scoringMethod DOENCH_2016 -filterGCmin 30 -filterGCmax 70 -filterSelfCompMax 1 -p 0”. Then, gRNA candidates meeting specific requirements were selected for synthesis. These criteria include: ( i ) absence of self-complementarity, ( ii ) absence of gRNA off-targets with mismatches ranging from 0 to 3 bases, ( iii ) predicted efficiencies greater than 50%; and ( iv ) target genic sites located within the initial third of the CDS region. If no suitable gRNAs meeting above criteria were found, the last criterion was adjusted to permit targeting sites within the first half of the CDS region. Subsequently, the gRNA library was synthesized by custom microarrays, amplified ( Table S1 ), and then ligated to the digested plasmid with the CRISPR/Cas system by infusion. The ligated plasmids were then transformed into E. coli , and over 30,000 colonies were selected to establish a plasmid library. The gRNA PCR library and the plasmid library were sequenced by NGS to evaluate their quality. The plasmid library was then transformed into N. oceanica , with the resulting transformants being transferred into a 24-well plate for subsequent analysis. High-throughput detection of mutation sites in the transformants by NGS To detect mutations induced by the CRISPR/Cas system, transformants in each 24-well plate were combined to perform shotgun genome sequencing. A tool (scmli; https://github.com/gongyh/scmli ) was developed to call the variants around gRNA-targeted sites. Raw reads were quality controlled using Trim Galore 65 with parameters “--paired -q 20 --trim-n --max_n 0 --length 70”. A modified version of snippy ( https://github.com/gongyh/scmli/blob/main/libs/snippy ; original version: https://github.com/tseemann/snippy ) was used to call preliminary SNPs and InDels with parameters “--basequal 30 --minqual 0.0 --minfrac 0.0”. Finally, variants located within or overlapping with the gRNA target regions, defined as -11 bp to + 5 bp relative to the theoretical cutting site, were retained for subsequent analysis. Single-cell sorting of carotenoid-overproducing mutants by FlowRACS The RACS process was conducted using a FlowRACS 3.0 instrument (Qingdao Single-cell Biotech, Qingdao, China) for N. oceanica mutant libraries and wild-type controls 29 , 30 . The FlowRACS instrument was equipped with a Nd:YAG 532 nm laser emitter as the excitation light source (20 mW was used in all the experiments), a 50× water objective (NA = 1.0, Olympus, JP) to focus the laser beam on the sample, and an electron-multiplying charge-coupled device (EMCCD) to collect SCRS. The cells were centrifuged at 2000 g and washed three times with sterile deionized water, collected and resuspended in 1% Pluronic F-127 (Sigma Aldrich, USA) for RACS. An alternating current (ac) of 18 volts peak to peak (Vp-p) at 1 MHz frequencies was applied to generate positive dielectrophoresis (pDEP) force to trap the cells, and the 532 nm laser power of 20 mW and acquisition time of 100 ms was used for obtaining SCRS. MDA amplification of CRISPR/Cas plasmids in the SCRS-based sorted cells The CRISPR/Cas plasmids in FlowRACS-screened N. oceanica mutants underwent amplification (MDA) with phi29 DNA polymerase 66 . Approx. 800 cells were sorted using FlowRACS 3.0 with a microfluidics chip (SCB-D002). The sorted cells were lysed with 1 µL lysis buffer (Buffer A) at 65°C for 10 min, followed by the addition of 1 µL stop solution (Buffer B). Subsequently, a 10× HotJa-reaction buffer (0.4 M Tris-HC1 (pH 7.6), 0.5 M KCl, 50 mM (NH 4 ) 2 SO 4 , 100 mM MgCl 2 , 40 mM DTT), dNTP (0.6 mM each), MDA-F1/F2/F3 primers (3 mM each), and HotJa Phi29 DNA Polymerase were added to each tube. The mixtures were thoroughly combined and incubated at 40°C for 8 hours with a heated lid at 70°C on a thermocycler. The amplified DNA served as the PCR template for amplifying gRNA target sites, and the resulting PCR products were sequenced using NGS. Enrichment analysis of gRNA sequences in the sorted genome-edited N. oceanica mutants Genomic RNAs were detected and quantified using the scmli package ( https://github.com/gongyh/scmli ). Significantly enriched gRNAs for each mutant library were identified using edgeR 67 , with a false discovery rate (FDR) of < 0.05 and a fold change of greater than 2. Gene Ontology (GO) and KEGG Orthology (KO) terms for the genes targeted by these gRNAs were retrieved from the NanDeSyn database ( https://nandesyn.single-cell.cn ), and enrichment analysis was performed using the clusterProfiler package 68 , 69 . Construction of the tRNA-based CRISPR/Cas plasmids for mutating the five noVDE s in N. oceanica To construct five knockout CRISPR/Cas systems for noVDE s, gRNA-HDV was amplified from pNOC-ARS-CRISPR-v2 31 , and glycine tRNA was amplified from the genome of N. oceanica to create a gRNA-HDV-tRNA structure by infusion. Four pairs of primers ( Table S1 ) were used to amplify the gRNA-HDV-tRNA structure, and the PCR products were ligated to the Bsp Q I-digested tRNA-based CRISPR/Cas backbone by infusion to generate the plasmid for transformation. Measurement of photosynthesis parameters for the wild-type and mutant N. oceanica Cells were cultured in flasks to reach the logarithmic phase at 23°C under continuous light (50 µmol photons m − 2 s − 1 ). Chlorophyll fluorescence of WT and mutants was measured using a pulse-amplitude modulated fluorometer (Image PAM, Walz, Effeltrich, Germany) after 20 min dark treatment of the cells. The maximum quantum yield of photosystem II (Fv/Fm) was measured as previously described 70 , 71 . Extraction and determination of pigments for the wild-type and mutant N. oceanica N. oceanica cells were collected via centrifugation, rinsed with deionized water, and extracted using ethanol. The ethanol extracts were then separated using a Thermo Scientific Vanquish high-performance liquid chromatography (HPLC) system equipped with a variable wavelength detector set at 450 nm for carotenoid detection and an Agilent 5 µm Eclipse XDB-C18 4.6 × 250 mm analytical column. Pigments were eluted at a flow rate of 1 mL min – 1 with a linear gradient from 100% Solvent A (85% methanol) to 70% Solvent B (ethyl acetate) over a 16 min period, followed by 2 min 70% Solvent B. Then pigments were eluted using a linear gradient from 30% Solvent A to 100% Solvent A over 2 min, followed by 10 min of Solvent A. Individual carotenoids were identified by their absorption spectra, and their typical retention times were compared to standard samples of pure carotenoids. RNA-Seq library construction for the wild-type and mutant N. oceanica Total RNA of noPAC4 mutants and WT cultured for five days was extracted using Trizol reagents (Tiangen, China). Concentration and purity of the RNA were determined spectrophotometrically (IMPLEN, USA), and RNA integrity was assessed with the RNA Nano 6000 Assay Kit on an Agilent Bioanalyzer 2100. A total of 2 µg of RNA per sample was used as input material for RNA sample preparation. Sequencing libraries were generated using the NEBNext Ultra™ RNA Library Prep Kit for Illumina (NEB, USA), and indices were added to associate sequences with each sample. The clustering of indexed samples was performed on a cBot Cluster Generation System using the HiSeq 3000/4000 PE Cluster Kit Box 1 (Illumina, USA). After cluster generation, the library preparations were sequenced on an Illumina HiSeq 4000, generating 150 bp paired-end reads. Computational analysis of transcriptome data from the wild-type and mutant N. oceanica RNA-seq datasets underwent quality control and then generation of gene expression profiles, based on the nfcore/rnaseq pipeline ( https://nf-co.re/rnaseq ). Specifically, reads were aligned with STAR, employing modified parameters to limit intron lengths (“--alignIntronMin 20, --alignIntronMax 3000”). Subsequently, Trinity 72 was utilized to generate a gene expression matrix using the RSEM2 method, with TMM normalization implemented to account for library size variations among samples. Then transcripts per million (TPM) values were averaged across replicates. For the mutant samples, DEGs were identified by edgeR 67 , via an FDR threshold of ≤ 0.001 and an FC of > 2. Statistical significance of TPMs across different categories was assessed by the Wilcoxon test. Gene set enrichment for GO terms and KEGG pathways was analyzed by clusterProfiler 68 , 69 , with a Benjamini-Hochberg adjusted p value of 0.05 set as the significance cutoff. Declarations Data availability All sequence data were deposited to the NCBI Sequence Read Archive (PRJNA1188048 for genomic data) and Gene Expression Omnibus (GSE281748 for transcriptomic data). To facilitate validation and foster collaboration, all gRNAs validated in this study were deposited into the NanDeSyn database (https://nandesyn.single-cell.cn/CRISPR_library), and are freely available upon request. Code availability Codes for detecting gRNAs and mutation sites are available via GitHub at https://github.com/gongyh/scmli. Author contributions JX, BM, QTW, and YHG designed the study. QTW, XFD, LHW, and NNL constructed the CRISPR/Cas mutant library. XXW and BM performed the FlowRACS. XFD and LHW phenotypes the mutants. YHG and JSZ analyzed the NGS data. ZN and YL participated in data interpretation and manuscript editing. JX, QTW, YHG and XXW wrote the manuscript. Acknowledgements This study was funded by the National Key Research and Development Program of China (No. 2021YFA0909700), National Natural Science Foundation of China (No. 32270109, 3217010815 and 32470087) and the CAS-CEA Partnership Program. We are also grateful to NaturalScience Foundation of Shandong Province (2021ZDSYS29 and ZR2024YQ048), Science and Technology Benefiting People Program of Qingdao (No. 23-2-8-smjk-3-nsh), CAS Young Scientists in Basic Research Program (No. YSBR-111), CAS Young-Scientist Innovation Promotion Association, and Shandong Taishan Scholarship. Competing interest JX and BM are on the scientific board of Qingdao Single-cell Biotechnology Co. No other competing interest is declared . References Falkowski, P.G. et al. The evolution of modern eukaryotic phytoplankton. Science 305 , 354-60 (2004). Khan, M.I., Shin, J.H. & Kim, J.D. The promising future of microalgae: current status, challenges, and optimization of a sustainable and renewable industry for biofuels, feed, and other products. Microb. Cell Fact. 17 , 36 (2018). Barkia, I., Saari, N. & Manning, S.R. Microalgae for high-value products towards human health and nutrition. Mar. Drugs 17 , 304 (2019). 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Springer Nature (2016). Hatzenpichler, R., Krukenberg, V., Spietz, R.L. & Jay, Z.J. Next-generation physiology approaches to study microbiome function at single cell level. Nat. Rev. Microbiol. 18 , 241-256 (2020). Li, J. et al. Choreography of transcriptomes and lipidomes of Nannochloropsis reveals the mechanisms of oil synthesis in microalgae. Plant Cell 26 , 1645-1665 (2014). Wang, Q. et al. Genome editing of model oleaginous microalgae Nannochloropsis spp. by CRISPR/Cas9. Plant J. 88 , 1071-1081 (2016). Krueger, F. et al. FelixKrueger/TrimGalore: v0.6.10 - add default decompression path. (2023). Zhang, J. et al. Improved single-cell genome amplification by a high-efficiency phi29 DNA polymerase. Front. Bioeng. Biotechnol. 11 , 1233856 (2023). Robinson, M.D., Mccarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 , 139 (2010). Wu, T. et al. ClusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2 , 100141 (2021). Xu, S. et al. Using clusterProfiler to characterize multiomics data. Nat. Protoc. 19 , 3292-3320 (2024). Wei, L. et al. RNAi-based targeted gene knockdown in the model oleaginous microalgae Nannochloropsis oceanica . Plant J. 89 , 1236-1250 (2017). Maxwell, K. & Johnson, G.N. Chlorophyll fluorescence-a practical guide. J. Exp. Bot. 51 , 659-68 (2000). Grabherr, M.G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29 , 644-652 (2011). Wei, L. et al. Transcriptomic and proteomic responses to very low CO 2 suggest multiple carbon concentrating mechanisms in Nannochloropsis oceanica . Biotechnol. Biofuels 12 , 168 (2019). Additional Declarations There is NO Competing Interest. Supplementary Files supplementaryTables.docx Supplementary Table S1，S2 SupplementalData1250630.xlsx The sequences of the genome-wide gRNA library targeting the exons of genes. SupplementalData2250630.xlsx The identification of mutation sites for the transformants from the mutant library by NGS of both gRNA and the genome in the transformants. SupplementalData3250630.xlsx The carotenoid-overproducing genes detected by NGS of screened microalgal cells via FlowRACS at the single-cell level. SupplementalData4250630.xlsx The significantly changed genes identified from the transcriptome data of noPAC4 mutants as compared to the WT. NannoCasLibFlowRACSfigall250701SF.pdf Figures S1-S6 Cite Share Download PDF Status: Under Review 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. 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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-7026720","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490571168,"identity":"436ac134-7070-471c-bbef-80f9966235c2","order_by":0,"name":"Jian 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qintao","middleName":"","lastName":"Wang","suffix":""},{"id":490571170,"identity":"2e13fcd1-3b7f-4966-9c3d-58e9a2781e39","order_by":2,"name":"Yanhai Gong","email":"","orcid":"https://orcid.org/0000-0003-2459-8724","institution":"Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yanhai","middleName":"","lastName":"Gong","suffix":""},{"id":490571171,"identity":"7073c765-0a54-4b37-83c9-87935089e66f","order_by":3,"name":"Lianhong Wang","email":"","orcid":"","institution":"Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lianhong","middleName":"","lastName":"Wang","suffix":""},{"id":490571172,"identity":"15564fce-1d42-40b2-8949-96b364a5ea45","order_by":4,"name":"Nana Lv","email":"","orcid":"","institution":"Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nana","middleName":"","lastName":"Lv","suffix":""},{"id":490571173,"identity":"472ee17e-5920-411b-9451-f2a0d5d307ed","order_by":5,"name":"Xuefeng Du","email":"","orcid":"","institution":"Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xuefeng","middleName":"","lastName":"Du","suffix":""},{"id":490571174,"identity":"234cd60c-58e3-4092-8398-4b4a433eb3b6","order_by":6,"name":"Jiashun Zhang","email":"","orcid":"","institution":"Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jiashun","middleName":"","lastName":"Zhang","suffix":""},{"id":490571175,"identity":"26cbab53-7c5a-4f7e-b64a-b5bc1ce44af5","order_by":7,"name":"Yi Xin","email":"","orcid":"","institution":"Qingdao Institute of Bioenergy and Bioprocess 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06:47:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3466476,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7026720/v1/dd2ce0b5-31ce-413c-8909-3a271921cc44.pdf"},{"id":87794806,"identity":"513f5679-331c-4037-af28-0c3189844a07","added_by":"auto","created_at":"2025-07-29 06:39:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22268,"visible":true,"origin":"","legend":"Supplementary Table S1\u0026#xFF0C;S2","description":"","filename":"supplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7026720/v1/7e8394edf7430e7995105456.docx"},{"id":87794807,"identity":"99756b93-5789-4e19-b27b-d8d85019a06c","added_by":"auto","created_at":"2025-07-29 06:39:00","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":351671,"visible":true,"origin":"","legend":"\u003cp\u003eThe sequences of the genome-wide gRNA library targeting the exons of genes.\u003c/p\u003e","description":"","filename":"SupplementalData1250630.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7026720/v1/43b314a8b1d92ff2e8ec2f39.xlsx"},{"id":87794808,"identity":"100d477f-35ca-4ca3-8d13-4657bb9dc17e","added_by":"auto","created_at":"2025-07-29 06:39:00","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":399353,"visible":true,"origin":"","legend":"\u003cp\u003eThe identification of mutation sites for the transformants from the mutant library by NGS of both gRNA and the genome in the transformants.\u003c/p\u003e","description":"","filename":"SupplementalData2250630.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7026720/v1/33c2d18c4d94b03e57b5b38a.xlsx"},{"id":87794805,"identity":"17993229-a18d-41cf-a34c-0e39f383d714","added_by":"auto","created_at":"2025-07-29 06:38:59","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":42628,"visible":true,"origin":"","legend":"\u003cp\u003eThe carotenoid-overproducing genes detected by NGS of screened microalgal cells via FlowRACS at the single-cell level.\u003c/p\u003e","description":"","filename":"SupplementalData3250630.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7026720/v1/5f4c7ca6637613ed2f05b027.xlsx"},{"id":87794809,"identity":"3a306926-c3a6-432f-94e8-91cde7fddcbc","added_by":"auto","created_at":"2025-07-29 06:39:00","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":22031,"visible":true,"origin":"","legend":"\u003cp\u003eThe significantly changed genes identified from the transcriptome data of noPAC4 mutants as compared to the WT.\u003c/p\u003e","description":"","filename":"SupplementalData4250630.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7026720/v1/b273bbaeae8a26e52e80c9a3.xlsx"},{"id":87794813,"identity":"a4bb4f87-cbc7-4883-8a26-99677ba8e65a","added_by":"auto","created_at":"2025-07-29 06:39:02","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1527853,"visible":true,"origin":"","legend":"Figures S1-S6","description":"","filename":"NannoCasLibFlowRACSfigall250701SF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7026720/v1/ffdd98ecc4e56c0c16af0272.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"High-throughput Raman-activated cell sorting of microalgal genome-wide edited library revealed a new regulatory pathway for carotenoid synthesis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicroalgae are unicellular photosynthetic organisms that generate over 45% of the planet\u0026rsquo;s annual net primary biomass from carbon dioxide and sunlight\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Moreover, they harbor diverse metabolic capabilities for synthesis of valuable compounds such as proteins, lipids, carbohydrates, vitamins, antioxidants, polyunsaturated fatty acids (PUFAs), and pigments\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Therefore, microalgae are promising cell factories for carbon-neutral production of nutraceuticals, pharmaceuticals and biofuels\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. To fully harness their metabolic capabilities, hundreds of microalgal genomes have been sequenced\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e; yet, one striking finding is the large number of functionally unknown genes. For example, in the model oleaginous microalga of \u003cem\u003eN. oceanica\u003c/em\u003e, over 50% of all predicted genes lack functional annotations, due to a lack of sufficient sequence similarity to other annotated genes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Moreover, even in the case of high sequence similarity to genes with experimentally validated functions, direct transfer of functional annotation in the absence of experimental verification is often problematic, since distinguishing the functions among homologous genes is difficult\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Such lack of functional validation for a vast fraction of genes has long hindered mechanistic understanding and metabolic engineering of microalgae.\u003c/p\u003e\u003cp\u003eTo identify genes that underlie a particular metabolic trait, forward genetics approaches generally start with mutagenesis, whereby mutations are induced in the genomes, followed by screening for specific phenotypic changes due to the mutations, and then mapping of the genes responsible for the observed phenotype. Such approaches have been widely applied in microalgae to identify genes implicated in lipid metabolism and photosynthesis\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, however limitations are profound. (\u003cem\u003ei\u003c/em\u003e) Mutant library construction has traditionally relied on genetic mutagenesis induced by physical or chemical mutagens, or via random insertion\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and identifying genotypes with the desired traits is usually extremely laborious and time-consuming\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Moreover, mutants in so-generated mutant libraries generally have multiple mutation sites, which impedes an unambiguous assignment of mutant phenotype to a specific genetic locus. Whilst random insertion can yield single-locus mutations, many of these mutations may occur in intergenic or non-coding sequences, which may generate very subtle phenotypes that are difficult to measure\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. (\u003cem\u003eii\u003c/em\u003e) Metabolic-phenotype-based screening poses a major challenge for microalgae. Whilst fluorescence-activated cell sorting (FACS) offers a valuable tool to address this challenge, its reliance on tedious fluorescent labeling for a specific protein or metabolite has hindered broad application and may introduce biases due to alteration of the natural state of cells\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Therefore, to increase throughput and reliability of forward genetics screens, label-free, high-throughput methods for sorting metabolic traits are urgently needed.\u003c/p\u003e\u003cp\u003e\u003cem\u003eNannochloropsis\u003c/em\u003e are unicellular algae known for their rapid growth, adaptability to various environmental conditions, high lipid content, and production of high-value metabolites such as triacylglycerols (TAG), eicosapentaenoic acid (EPA), and carotenoids\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Employing the industrial oleaginous microalga of \u003cem\u003eN. oceanica\u003c/em\u003e as a model, we introduced a new forward genetics platform that consists of a genome-wide single-gene-edited mutant library and high-throughput Raman-activated Cell Sorting (RACS). By generating the first such library for microalgae and then sorting it via RACS, we rapidly uncovered dozens of genes whose mutations reduced carotenoid contents, despite the lack of previous evidence linking them to carotenogenesis. In particular, two previously unknown modes of carotenogenesis regulation mediated by proteasome assembly chaperone 4 (\u003cem\u003enoPAC4\u003c/em\u003e) were revealed, which involves epigenetic mechanisms via histone deacetylase (HDAC) and post-translational controls by the 26S proteasome. By tracing single-cell metabolic phenotype to single-gene genotype in a label-free, high-throughput manner, the platform can greatly accelerate the discovery of new functional genes and pathways in microalgae and other organisms.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eFunctional gene mining by sorting a single-gene-mutant library via single-cell metabolic phenotypes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe classical approaches to construct a genetic mutant library rely on either random insertion (e.g., using Tn5\u003csup\u003e23\u003c/sup\u003e) or application of physical or chemical mutagens\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. To screen for a metabolic phenotype (e.g., content of metabolites like lipids\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e), a mutant library is usually analyzed based on colonies grown on plate by mass spectroscopy or via biochemically/genetically introduced fluorescence probes by FACS (\u003cb\u003eFig.\u0026nbsp;1A\u003c/b\u003e). In contrast, we propose a strategy that features the creation of targeted, genome-wide single-gene-mutated strains by programmable gene editing, label-free sorting of single-cell metabolic phenotypes by RACS, and then establishing the single-cell genotype-phenotype links by sequencing the sorted cells (\u003cb\u003eFig.\u0026nbsp;1B\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThe platform consists of two main modules. (\u003cem\u003ei\u003c/em\u003e) A genome-wide single-gene targeted mutagenesis library for \u003cem\u003eN. oceanica\u003c/em\u003e was created by the CRISPR/Cas system, which greatly simplifies large-scale single-gene editing (and thus mutagenesis) due to its targeted-mutagenesis nature, as well as its efficiency, simplicity, and programmability\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Moreover, mutation sites can be tracked using the gRNA sequence, which is easily detectable. No such libraries have previously been reported for microalgae\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, despite the successes in various higher plants such as rice, tomato, soybean, and maize\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. (\u003cem\u003eii\u003c/em\u003e) A metabolism-driven label-free single-cell screening platform was established for \u003cem\u003eN. oceanica\u003c/em\u003e based on high-throughput RACS. A spontaneous single-cell Raman spectrum (SCRS) provides the intrinsic biochemical profile of a cell at a given state and thus can be considered as a function-based snapshot of the molecular state of the cell. Importantly, this strategy is label-free, culture-free, noninvasive, rich in metabolic information, and broadly applicable\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. For instance, the FlowRACS system that we developed for high-throughput RACS can profile and sort fungal and human cells based on SCRS\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of a CRISPR/Cas system-based genome-wide mutant library of\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo generate a CRISPR/Cas-based mutant library of \u003cem\u003eN. oceanica\u003c/em\u003e (\u003cb\u003eFig.\u0026nbsp;2A\u003c/b\u003e; \u003cb\u003eMethods\u003c/b\u003e), a gRNA library was designed, synthesized and subsequently ligated to plasmids containing the CRISPR/Cas system. This plasmid library was transformed into \u003cem\u003eN. oceanica\u003c/em\u003e to construct the mutant library, which was then sequenced using next-generation sequencing (NGS) to identify the mutation sites. Precise generation of gRNAs was ensured by engineering them with hammerhead (HH) self-cleaving ribozymes at the 5\u0026rsquo; end and hepatitis delta virus (HDV) self-cleaving ribozymes at the 3\u0026rsquo; end in the episome-based CRISPR/Cas system in \u003cem\u003eN. oceanica\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The HH structure dictates that its first six bases must correspond to the reverse complement of the first six bases of the gRNA sequence (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e, purple). After the ligation of the gRNA target sequence library to the plasmid containing the CRISPR/Cas system via infusion, we sequenced 15 colonies and identified just one accurately ligated plasmid (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e, green): such observed low accuracy impeded the knockout efficiency in constructing the mutant library. It seemed that the loop structure necessary for HH self-assembly obstructs the correct ligation of the gRNA PCR library to the plasmid that harbors the CRISPR/Cas system.\u003c/p\u003e\u003cp\u003eTo streamline the design and ligation of the gRNA pool to the plasmid and to ensure accurate gRNA expression, we employed tRNA from glycine in \u003cem\u003eN. oceanica\u003c/em\u003e instead of HH self-cleaving ribozymes (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/b\u003e; \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). To evaluate the knock-out efficiency, a light-harvesting complex protein (LHC; NO13G02860) was chosen as the target gene. Sequencing of the target sites in twelve transformants revealed mutations in each of them (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/b\u003e), indicating a high-efficiency of tRNA-based CRISPR/Cas system in \u003cem\u003eN. oceanica\u003c/em\u003e. Moreover, the accuracy of gRNA target sequence ligation to the system is 85%, as 17 among the 20 sequenced colonies were correct (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC\u003c/b\u003e, blue).\u003c/p\u003e\u003cp\u003eNext, we assessed the coverage, uniformity, and accuracy of the plasmid library which underlie the quality of the eventual tRNA-based CRISPR/Cas mutant library. These factors depend on the design, synthesis, and ligation (to the CRISPR/Cas-encoded plasmid) of the gRNA library. To maximize the coverage, a total of 9,709 gRNAs were designed to span\u0026thinsp;~\u0026thinsp;94% of the entire protein-coding sequences (\u003cb\u003eSupplemental Data 1\u003c/b\u003e; \u003cb\u003eMethods\u003c/b\u003e). The gRNA target sites from both libraries were amplified (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) and sequenced (\u003cb\u003eFig.\u0026nbsp;2B\u003c/b\u003e), indicating 92% accuracy of the gRNAs in the gRNA PCR library, and 84% in the plasmid library (\u003cb\u003eFig.\u0026nbsp;2C\u003c/b\u003e). Furthermore, in the plasmid library, all designed gRNAs were represented (\u003cb\u003eFig.\u0026nbsp;2C\u003c/b\u003e), with a high degree of uniformity in the distribution of gRNA abundance (Gini index of 0.04 for the plasmid library and 0.02 for the gRNA PCR library; \u003cb\u003eFig.\u0026nbsp;2D\u003c/b\u003e), thus the library is well-balanced and each gRNA is represented at a similar level. These findings confirm the high quality of the plasmid library for a CRISPR/Cas system-based genome-wide single-gene mutated library of \u003cem\u003eN. oceanica\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHigh mutational efficiency and low bias in mutated genes in the\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e \u003cb\u003emutant library\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter the transformation, a total of 5,760 transformants were distributed among 240 24-well plates, and mutation sites in each well was identified via NGS (\u003cb\u003eFig.\u0026nbsp;3\u003c/b\u003e). Specifically, the 24 transformants from each 24-well plate were pooled for whole genome sequencing to map the mutations present within these genomes (\u003cb\u003eFig.\u0026nbsp;3A\u003c/b\u003e). However, the existence of natural mutations in the genome and sequencing errors posed challenges in accurate detection of mutation sites. To tackle this issue, the gRNA from each well was examined to distinguish the CRISPR/Cas-induced mutation sites. Microalgal transformants in the 240 24-well plates were organized into ten pools, with each pool (24 plates) carefully collected (\u003cb\u003eFig.\u0026nbsp;3B\u003c/b\u003e). To confirm the gRNA composition within each well, the gRNAs were amplified using PCR (primers in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) and sequenced by NGS. Then the gRNAs in each well were verified based on their probability of occurrence.\u003c/p\u003e\u003cp\u003eBy aligning the gRNAs against the genome sequence at the designated target sites in each well, we found that 2,851 of the 5,760 colonies exhibited exactly one mutated gene, 716 colonies displayed two or more mutated genes, and 2,193 colonies showed no mutated genes (\u003cb\u003eSupplemental Data 2\u003c/b\u003e; \u003cb\u003eFig.\u0026nbsp;3C\u003c/b\u003e). Collectively, 2,397 effective gRNAs were identified (\u003cb\u003eMethods\u003c/b\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nandesyn.single-cell.cn/CRISPR_library\u003c/span\u003e\u003cspan address=\"https://nandesyn.single-cell.cn/CRISPR_library\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), indicating 41.6% efficiency for obtaining valid gRNAs among the total mutants (5,760 mutants, \u003cb\u003eFig.\u0026nbsp;3D\u003c/b\u003e). The mutations were categorized into nine distinct classes: 3,663 frameshift-variants, 214 missense-variants, 115 disruptive-inframe-deletions, 78 synonymous-variants, 54 conservative-inframe-deletions, 27 disruptive-inframe-insertions, 13 conservative-inframe-insertions, 10 stop-gained, and 117 variants of multiple types (\u003cb\u003eFig.\u0026nbsp;3D\u003c/b\u003e). A substantial majority of the mutations (85.4%) were classified as frameshift-variants, which significantly contribute to the generation of loss-of-function mutants for reverse genetics in \u003cem\u003eN. oceanica.\u003c/em\u003e These results validate the successful construction of a CRISPR/Cas-based \u003cem\u003eN. oceanica\u003c/em\u003e mutant library, providing 3,567 mutated colonies and 2,397 efficient gRNAs for subsequent genome editing and mutant screening endeavors.\u003c/p\u003e\u003cp\u003eQuantitative analyses of the library revealed 78.1% of its mutated genes as unique among the mutant strains (\u003cb\u003eFig.\u0026nbsp;4A\u003c/b\u003e), slightly lower than the theoretically expected probability of 84%. This discrepancy may be attributed to the presence of lethal genes that cannot yield viable mutants. The average proximity between adjacent mutated genes is 12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 kb, showing no significant deviation from random sampling (Wilcoxon test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; \u003cb\u003eFig.\u0026nbsp;4B\u003c/b\u003e). Based on the Clusters of Orthologous Genes (COG) database, only the COG category of \u0026ldquo;translation, ribosomal structure, and biogenesis\u0026rdquo; exhibits significant difference (in this case, lower-than-expected representation) from the target proteome (Chi-square test, adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003cb\u003eFig.\u0026nbsp;4C\u003c/b\u003e), which may be due to a higher prevalence of lethal genes within this category. Overall, the mutant library exhibits high quality, characterized by a uniform distribution of mutated genes that is genome wide and among the functional categories of proteome.\u003c/p\u003e\u003cp\u003eFurthermore, to evaluate the effect of gRNA base preference on editing efficacy in \u003cem\u003eN. oceanica\u003c/em\u003e, we investigated the nucleotide distribution at different positions within the designed and effective gRNAs (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). No discernible preference for specific nucleotides was found at any position when compared with the designed and effective gRNAs (Z-test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; \u003cb\u003eFig.\u0026nbsp;4D\u003c/b\u003e), indicating the efficient design of gRNA library. Given the high uniformity of the mutant library, we observed significant positive Pearson correlation between the number of mutated genes and the number of transformants (\u003cb\u003eFig.\u0026nbsp;4E\u003c/b\u003e), suggesting a saturated mutant library can be constructed by collecting additional mutant clones. Thus, a high-quality genome-wide knockout mutant library of \u003cem\u003eN. oceanica\u003c/em\u003e was constructed with high efficiency and high uniformity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHigh-throughput SCRS-based sorting identified mutated genes underlying carotenoid overproduction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCarotenoids, a class of terpenoid pigments with C40 backbones found in microalgae and plants, are pivotal to photosynthesis and widely used in many industries due to their health benefits\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Traditional screening for carotenogenesis genes typically involves time-consuming, labor-intensive, and low-throughput culturing of mutants and measuring changes in carotenoid levels from bulk biomass, before identifying target genes (\u003cb\u003eFig.\u0026nbsp;5A\u003c/b\u003e)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In contrast, we propose that SCRS-based sorting of the genome-wide knockout mutant library enables single-cell-resolved, high-throughput screening of mutants with high carotenoid content, thus greatly accelerating function-directed gene mining (\u003cb\u003eFig.\u0026nbsp;5A\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eCarotenoids exhibit characteristic resonance Raman peaks near ~\u0026thinsp;1520 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, ~\u0026thinsp;1156 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and ~\u0026thinsp;1006 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, whereas chlorophyll shows no peaks in these regions\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, thus intracellular carotenoids can be detected without interference from chlorophyll signals in photosynthetic cells such as \u003cem\u003eChlamydomonas reinhardtii\u003c/em\u003e and \u003cem\u003eHaematococcus pluvialis\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In \u003cem\u003eN. oceanica\u003c/em\u003e, zeaxanthin, violaxanthin, and β-carotene are the three major carotenoids, collectively accounting for approx.75% of the total carotenoids\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Raman spectra of a gradient of these individual purified carotenoids revealed the concentrations of β-carotene, violaxanthin, and zeaxanthin are linearly dependent (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.99, 1, and 1, respectively) on normalized intensities at 1511 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e1511\u003c/sub\u003e - \u003cem\u003eI\u003c/em\u003e\u003csub\u003e1800\u003c/sub\u003e), 1522 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e1522\u003c/sub\u003e - \u003cem\u003eI\u003c/em\u003e\u003csub\u003e1800\u003c/sub\u003e), and 1517 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e1517\u003c/sub\u003e - \u003cem\u003eI\u003c/em\u003e\u003csub\u003e1800\u003c/sub\u003e; 1800 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e used as baseline due to the absence of biological molecules vibrations from 1800 to 1840 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; \u003cb\u003eFig.\u0026nbsp;5B-D\u003c/b\u003e). We then profiled the SCRS of the \u003cem\u003eN. oceanica\u003c/em\u003e cells from the CRISPR/Cas mutant library (after five-day cultured), with the 532 nm laser and throughput of 600 cells min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in FlowRACS (\u003cb\u003eFig.\u0026nbsp;5A\u003c/b\u003e). The microalgal SCRS showed three most prominent bands at 1509 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1154 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 996 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (\u003cb\u003eFig.\u0026nbsp;5E\u003c/b\u003e), which agree with the carotenoid standards and support using these peaks for carotenoid quantification in \u003cem\u003eN. oceanica\u003c/em\u003e. Among these peaks, 1509 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (C\u0026thinsp;=\u0026thinsp;C bond stretching vibrations in carotenoids) showed the highest intensity, thus the \u0026ldquo;\u003cem\u003eI\u003c/em\u003e\u003csub\u003e1,509\u003c/sub\u003e - \u003cem\u003eI\u003c/em\u003e\u003csub\u003e1,800\u003c/sub\u003e \u0026gt;\u003cem\u003ex\u003c/em\u003e\u0026rdquo; criterion was applied to sort carotenoid-overproducing cells from the mutant library.\u003c/p\u003e\u003cp\u003eTo determine the sorting threshold of \u0026ldquo;\u003cem\u003ex\u003c/em\u003e\u0026rdquo;, over 1,000 cells were first measured in a single FlowRACS run, while the value of \u003cem\u003eI\u003c/em\u003e\u003csub\u003e1,509\u003c/sub\u003e - \u003cem\u003eI\u003c/em\u003e\u003csub\u003e1,800\u003c/sub\u003e in SCRS that separates the top 1% of cells in intracellular carotenoid content from the remaining 99% of cells was chosen as the \u0026ldquo;\u003cem\u003ex\u003c/em\u003e\u0026rdquo;. Given that direct detection of target sites was challenging due to natural variations and sequencing errors, we relied on gRNAs as markers for identifying mutated genes. Considering a throughput of ~\u0026thinsp;600 cells min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e while retaining only the top 1% of cells, the sorted cells during five hours of screening were subjected to DNA amplification by Multiple Displacement Amplification (MDA) with primers targeting the plasmid within the CRISPR/Cas system (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e; \u003cb\u003eMethods\u003c/b\u003e), then the MDA products were amplified by PCR and sequenced by NGS to analyze the gRNA abundance. In addition, the gRNA sequences in the unsorted mutant library were amplified by PCR and sequenced by NGS as a control.\u003c/p\u003e\u003cp\u003eThe screening experiment is divided into two steps. First, to determine whether there are mutant strains with the target phenotype in the mutant library, over 10,000 microalgal cells from the library and a wild-type sample (WT) were subjected to FlowRACS. The SCRS obtained from the mutant library exhibited significantly higher heterogeneity than the WT, as evidenced by the broader variations in Raman intensities across different Raman shifts (\u003cb\u003eFig.\u0026nbsp;5E-F\u003c/b\u003e). These variations encompassed, but were not limited to, characteristic carotenoid Raman peaks (\u003cem\u003ev\u003c/em\u003e1: ~996 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; \u003cem\u003ev\u003c/em\u003e2: ~1,154 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; \u003cem\u003ev\u003c/em\u003e3: ~1,509 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Indeed, the standard deviations of Raman intensities at each Raman shift were on average 1.69-fold higher in the mutant library than in the WT (Wilcoxon test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; \u003cb\u003eFig.\u0026nbsp;5F\u003c/b\u003e). Moreover, the \u003cem\u003ev\u003c/em\u003e3 peak intensities in the mutant library were averagely 1.61-fold higher than in the WT (Wilcoxon test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; \u003cb\u003eFig.\u0026nbsp;5G\u003c/b\u003e), suggesting that the increase in carotenoid content in at least some of the cells in the mutant library. Notably, following subsampling of 4,000 cells for both the mutant library and the WT sample, the screening identified 26 mutants with intensity exceeding 6,000 at 1,509 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, whereas only four cells in the WT sample exhibited similarly high Raman intensities at the \u003cem\u003ev3\u003c/em\u003e peak (\u003cb\u003eFig.\u0026nbsp;5H\u003c/b\u003e). These results suggested the presence of many carotenoid-overproducing mutants in the library, and supported the suitability of FlowRACS for screening such metabolic phenotypes in microalgae.\u003c/p\u003e\u003cp\u003eSecond, we sorted carotenoid-overproducing mutants through three independent FlowRACS runs based on \u003cem\u003ev\u003c/em\u003e3 peak intensities. Based on the fold-change of gRNA abundances before- and post-sorting (\u003cb\u003eMethods\u003c/b\u003e), 40, 44, and 56 genes were found enriched respectively (\u003cb\u003eSupplemental data 3\u003c/b\u003e). These mutated genes are related to over 20 pathways including carotenoid biosynthesis (e. g., \u003cem\u003enoVDEs\u003c/em\u003e) and photosynthesis (\u003cb\u003eFig.\u0026nbsp;5I\u003c/b\u003e; \u003cb\u003eSupplemental data 3\u003c/b\u003e), suggesting a direct or pleiotropic link between carotenoid biosynthesis and other metabolic pathways. Moreover, among the collectively 109 unique mutated genes (\u003cb\u003eSupplemental data 3\u003c/b\u003e), 78 are of unknown function, nine of which were consistently identified across all three independent runs, while 13 were detected in two of the three runs (\u003cb\u003eFig.\u0026nbsp;5I-J\u003c/b\u003e). Most of the shared genes seem not directly linked to carotenoid biosynthesis, with eight of them classified as unknown genes, i.e., suggesting novel mechanisms.\u003c/p\u003e\u003cp\u003eAmong the genes shared by at least two of the three FlowRACS runs are two violaxanthin de-epoxidases (\u003cem\u003eVDE\u003c/em\u003es), which may be responsible for the de-epoxidation of violaxanthin within the thylakoid membrane in the carotenoid biosynthesis pathway. Specifically, in higher plants, \u003cem\u003eVDE\u003c/em\u003e is activated under high light and converts violaxanthin to antheraxanthin and zeaxanthin, resulting in a decrease in violaxanthin content\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e; thus \u003cem\u003eVDE\u003c/em\u003e knock-out may result in the reduction of violaxanthin (and thus of carotenoids). In \u003cem\u003eN. oceanica\u003c/em\u003e, violaxanthin content can be regulated by carotenoid biosynthetic genes such as carotenoid isomerase (from \u003cem\u003ePhaeodactylum tricornutum\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e) and zeaxanthin epoxidase (\u003cem\u003eZEP\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, yet functional roles of the \u003cem\u003enoVDE\u003c/em\u003es are still unclear.\u003c/p\u003e\u003cp\u003eMoreover, many of the genes represent entirely unexpected links to carotenoid metabolism. For instance, NO02G03540, the proteasome assembly chaperone 4 (\u003cem\u003ePAC4\u003c/em\u003e), was among those with the highest enrichment (with log-fold changes of 16.8, 6.8, and 16.4 in the three replicates; \u003cb\u003eFig.\u0026nbsp;5I\u003c/b\u003e). This is a protein that facilitates biogenesis of the 26S proteasome\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, a large protein complex responsible for degrading ubiquitin-dependent proteins in eukaryotic cells\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003ePAC4\u003c/em\u003e and other proteasome assembly chaperones are known as potential targets for inhibiting accurate proteasome assembly in cancer cells\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In plants, chloroplast-targeted preproteins may undergo ubiquitin-dependent degradation by the 26S proteasome to prevent their accumulation in the cytosol and help shaping the chloroplast proteome\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. However, no evidence has ever implicated \u003cem\u003ePAC4\u003c/em\u003e in pigment metabolism.\u003c/p\u003e\u003cp\u003eCollectively, direct phenotypic screening of the \u003cem\u003eN. oceanica\u003c/em\u003e genome-wide targeted mutant library by FlowRACS rapidly uncovered many unexpected genes associated with elevation in single-cell carotenoid content. To dissect the underlying mechanisms, we selected \u003cem\u003eVDE\u003c/em\u003es and \u003cem\u003ePAC4\u003c/em\u003e as targets for validating \u003cem\u003ein vivo\u003c/em\u003e activities via metabolic engineering.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVDEs contribute to accumulation of carotenoids in\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo probe the \u003cem\u003ein vivo\u003c/em\u003e function of \u003cem\u003enoVDE\u003c/em\u003es, we started from the time-series transcriptomes of \u003cem\u003eN. oceanica\u003c/em\u003e under six conditions, including: very low carbon levels (VLC, 100 ppm CO\u003csub\u003e2\u003c/sub\u003e), high carbon levels (HC, 5% CO\u003csub\u003e2\u003c/sub\u003e), nitrogen-depleted (N-), nitrogen-replete (N+), exposure to red or blue light (\u003cb\u003eFig. S3A\u003c/b\u003e; \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nandesyn.single-cell.cn\u003c/span\u003e\u003cspan address=\"https://nandesyn.single-cell.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Under VLC, \u003cem\u003enoVDE1-5\u003c/em\u003e transcript levels (\u003cem\u003enoVDE1\u003c/em\u003e: NO24G00840, \u003cem\u003enoVDE2\u003c/em\u003e: NO20G02350, \u003cem\u003enoVDE3\u003c/em\u003e: NO11G00920, \u003cem\u003enoVDE4\u003c/em\u003e: NO03G00410, \u003cem\u003enoVDE5\u003c/em\u003e: NO08G01830) are 30.9%, 7.5%, 33.7%, 62.3%, and 136% higher than HC, respectively. This may be attributed to insufficient carbon supply for the dark reactions of photosynthesis under VLC. The excess absorption of light by photosynthetic pigments, relative to the cellular metabolic capacity, drives the overreduction of the photosynthetic electron transport chain, resulting in the generation of reactive oxygen species (ROS)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Thus, the activation of \u003cem\u003enoVDE\u003c/em\u003es can facilitate the conversion of violaxanthin to zeaxanthin, which enhances non-photochemical quenching (NPQ) and aids in the scavenging of chlorophyll triplets and ROS\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. These results support the roles of \u003cem\u003enoVDE\u003c/em\u003es in carotenoid biosynthesis in \u003cem\u003eN. oceanica\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eTo avoid compensatory effects of genes with similar function, a tRNA-based CRISPR/Cas system was employed to simultaneously mutate all predicted \u003cem\u003enoVDE\u003c/em\u003es using a single plasmid, resulting in two mutants, termed \u003cem\u003enovde-1\u003c/em\u003e and \u003cem\u003enovde-2\u003c/em\u003e (\u003cb\u003eFig.\u0026nbsp;6A\u003c/b\u003e), each of which carries frameshift mutations in all five \u003cem\u003enoVDE\u003c/em\u003es (except \u003cem\u003enoVDE4\u003c/em\u003e in \u003cem\u003enovde-1\u003c/em\u003e which carries a 18-base deletion). These mutants showed significant alterations in photosynthetic performance, plus in the levels of the violaxanthin and β-carotene (\u003cb\u003eFig.\u0026nbsp;6B-D\u003c/b\u003e). Under low light conditions, the Fv/Fm ratios for \u003cem\u003enovde-1\u003c/em\u003e and \u003cem\u003enovde-2\u003c/em\u003e increased by 4.0% and 3.4%, respectively, compared to the WT (\u003cb\u003eFig.\u0026nbsp;6B\u003c/b\u003e). In addition, the violaxanthin levels were increased by 43.3% and 24.8% (\u003cb\u003eFig.\u0026nbsp;6C\u003c/b\u003e), while β-carotene levels increased by 29.8% and 30.9% (\u003cb\u003eFig.\u0026nbsp;6D\u003c/b\u003e), respectively. Furthermore, the total content of violaxanthin, β-carotene and zeaxanthin increased by 21.7% and 11.4%, respectively (\u003cb\u003eFig.\u0026nbsp;6F\u003c/b\u003e). Under high light, compared to the WT, the Fv/Fm ratios in \u003cem\u003enovde-1\u003c/em\u003e and \u003cem\u003enovde-2\u003c/em\u003e increased by 8.3% and 8.1%, respectively (\u003cb\u003eFig.\u0026nbsp;6B\u003c/b\u003e), while the β-carotene levels increased by 35.6% and 34.2%, respectively (\u003cb\u003eFig.\u0026nbsp;6C\u003c/b\u003e). Moreover, the changes were not accompanied by significant alterations in growth rate, biomass, chlorophyll \u003cem\u003ea\u003c/em\u003e, or zeaxanthin content, with the exception that the biomass of \u003cem\u003enovde-2\u003c/em\u003e decreased by 7.8% (\u003cb\u003eFig. S4A-C\u003c/b\u003e; \u003cb\u003eFig.\u0026nbsp;6E\u003c/b\u003e). These findings validate the role of \u003cem\u003enoVDE\u003c/em\u003es in converting violaxanthin to zeaxanthin, and explain the accumulation of violaxanthin and higher carotenoid contents in \u003cem\u003enoVDE\u003c/em\u003es-knockout mutants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiscovery of a novel role of PAC4 that enhances carotenoid synthesis in\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNO02G03540 encodes noPAC4, putatively a proteasome assembly chaperone involved in assembly of the 26S proteasome. noPAC4 shows 37% sequence identity to human PAC4 (NCBI Reference Sequence: NP_001129222.1), yet no significant similarity to yeast PBA4 (GenBank: DAA11291.1). Given the lack of evidence linking \u003cem\u003enoPAC4\u003c/em\u003e to carotenoid biosynthesis in any organisms, we analyzed the rich \u003cem\u003eN. oceanica\u003c/em\u003e transcriptomes collected under diverse conditions in the NanDeSyn database\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, to probe mechanisms of the high-carotenoid phenotype in \u003cem\u003enoPAC4\u003c/em\u003e-edited mutants (\u003cb\u003eMethods\u003c/b\u003e). Under HC and VLC conditions, at 24 hours, \u003cem\u003enoPAC4\u003c/em\u003e expression is 51% lower under VLC than HC (\u003cb\u003eFig. S3A\u003c/b\u003e), while\u0026thinsp;~\u0026thinsp;75% of the 52 genes underlying 26S proteasome were downregulated (\u003cb\u003eFig. S3B\u003c/b\u003e). Notably, the \u003cem\u003enoPAC4\u003c/em\u003e transcript shows a strong positive correlation (median Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.88) with those of 26S proteasome genes over 3, 6, 12, and 24 hours (\u003cb\u003eFig. S3B\u003c/b\u003e), suggesting functional connections between \u003cem\u003enoPAC4\u003c/em\u003e regulation, 26S proteasome activity and carotenoid biosynthesis. We propose that enhanced protein biosynthesis under HC conditions raises 26S proteasome activity, which then alters carotenoid biosynthesis.\u003c/p\u003e\u003cp\u003ePhytoene synthase (\u003cem\u003ePSY\u003c/em\u003e) catalyzes carotenogenesis by condensing two molecules of geranylgeranyl diphosphate (GGPP) to produce the C40 hydrocarbon 15-cis-phytoene. Acting as the first committed step in carotenogenesis, \u003cem\u003ePSY\u003c/em\u003e directs the metabolic flux into the carotenoid biosynthetic pathway. The precursor protein of \u003cem\u003ePSY\u003c/em\u003e can be ubiquitinated by the plastid protein-sensing ring E3 ligase 1 (\u003cem\u003ePPSR1\u003c/em\u003e) and subsequently degraded by the 26S proteasome\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. We hypothesize that the knockout of \u003cem\u003enoPAC4\u003c/em\u003e decreases the efficacy of the 26S proteasome, resulting in an increased transport of precursor proteins of \u003cem\u003ePSY\u003c/em\u003e into the chloroplast, which in turn leads to enhanced carotenoid biosynthesis.\u003c/p\u003e\u003cp\u003eTo test these hypotheses, we independently knocked out \u003cem\u003enoPAC4\u003c/em\u003e (\u003cb\u003eFig.\u0026nbsp;7A\u003c/b\u003e). The mutants of \u003cem\u003enopac4-1\u003c/em\u003e and \u003cem\u003enopac4-2\u003c/em\u003e exhibit significant changes in growth rate, photosynthesis, and carotenoid content (\u003cb\u003eFig.\u0026nbsp;7B-F, S4D-E\u003c/b\u003e). Specifically, under low light (50 \u0026micro;mol photons m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) conditions, growth rate increased by 4.3% and 8.6%, respectively, compared to WT, while biomass decreased by 13.5% and 9.9%, respectively (\u003cb\u003eFig. S4D-E\u003c/b\u003e). The Fv/Fm ratios for \u003cem\u003enopac4-1\u003c/em\u003e and \u003cem\u003enopac4-2\u003c/em\u003e increased by 5.2% and 6.1%, respectively (\u003cb\u003eFig.\u0026nbsp;7B\u003c/b\u003e). Moreover, violaxanthin levels increased by 15.6% and 12.3% (\u003cb\u003eFig.\u0026nbsp;7C\u003c/b\u003e); β-carotene levels increased by 18.7% and 30.2% (\u003cb\u003eFig.\u0026nbsp;7D\u003c/b\u003e); and zeaxanthin levels increased by 16.6% and 22.1% (\u003cb\u003eFig.\u0026nbsp;7E\u003c/b\u003e), respectively. Furthermore, the total content of violaxanthin, β-carotene and zeaxanthin increased by 19.6% and 19.5%, respectively (\u003cb\u003eFig.\u0026nbsp;7F\u003c/b\u003e). However, under high light (300 \u0026micro;mol photons m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), no such difference was observed between WT and mutants, except for the 8.4% and 7.6% reduction in zeaxanthin levels in the mutants (\u003cb\u003eFig.\u0026nbsp;7B-E\u003c/b\u003e). The chlorophyll \u003cem\u003ea\u003c/em\u003e content was unchanged (\u003cb\u003eFig. S4F\u003c/b\u003e), suggesting that the SCRS-based mutant screening was specifically based on carotenoid accumulation yet independent of chlorophyll fluctuations. Therefore, the substantial increase in carotenoid levels in \u003cem\u003enoPAC4\u003c/em\u003e-knockout mutants under low light is consistent with the single-cell Raman-based phenotypes as identified and sorted. Moreover, the distinct physiological responses to \u003cem\u003enoPAC4\u003c/em\u003e knock-out from \u003cem\u003enoVDEs\u003c/em\u003e knock-outs suggest FlowRACS can identify mutants with very different mechanisms that underlie a target phenotype.\u003c/p\u003e\u003cp\u003eFurthermore, we profiled the transcriptomes of \u003cem\u003enoPAC4-\u003c/em\u003eknockout mutants and WT under low light via RNA-Seq (in triplicates; \u003cb\u003eFig. S5\u003c/b\u003e). In the mutants (\u003cb\u003eSupplemental data 4\u003c/b\u003e), 73 differentially expressed genes (DEGs) were identified compared to the WT. For example, a histone deacetylase (\u003cem\u003eHDAC\u003c/em\u003e, NO11G01600) is much lower in transcript level than the WT, by 43.0% and 54.8% in \u003cem\u003enopac4-1\u003c/em\u003e and \u003cem\u003enopac4-2\u003c/em\u003e, respectively (\u003cb\u003eFig.\u0026nbsp;7G\u003c/b\u003e). \u003cem\u003eHDAC\u003c/em\u003e interference accelerates the ripening process and increases β-carotene accumulation in tomato\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, raising the possibility that the reduced expression of \u003cem\u003eHDAC\u003c/em\u003e may also promote carotenoid biosynthesis in \u003cem\u003eN. oceanica\u003c/em\u003e. In addition, transcripts of retinol dehydrogenase (\u003cem\u003eRDH\u003c/em\u003e, NO04G04240) are 64.5% and 64.4% lower than that of WT, respectively (\u003cb\u003eFig.\u0026nbsp;7G\u003c/b\u003e). \u003cem\u003eRDH\u003c/em\u003e β-carotene 15,15\u0026rsquo;-dioxygenase (\u003cem\u003eBCO\u003c/em\u003e) cleaves β-carotene into retinal, which is then reduced to retinol by the retinol dehydrogenase\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The decreased expression of \u003cem\u003eRDH\u003c/em\u003e may reduce the cleavage of β-carotene, resulting in increased carotenoid accumulation. In addition, the expression of proteasome-associated genes was up-regulated in both \u003cem\u003enopac4-1\u003c/em\u003e and \u003cem\u003enopac4-2\u003c/em\u003e, as indicated by the KEGG gene set enrichment analysis (\u003cb\u003eFig.\u0026nbsp;7H-I\u003c/b\u003e and \u003cb\u003eFig. S6\u003c/b\u003e; \u003cb\u003eMethods\u003c/b\u003e), suggesting noPAC4-mediated regulation of 26S proteasome activity. Collectively, these results support the attenuation of 26S proteasome function by noPAC4 knockout, which may enhance the import of precursor proteins (e.g., PSY) into the chloroplast, thereby stimulating carotenoid biosynthesis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn \u003cem\u003eN. oceanica\u003c/em\u003e, carotenoid biosynthesis starts with the condensation of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), derived from the methylerythritol phosphate (MEP) pathway, to form the 20-carbon geranylgeranyl diphosphate (GGPP). This reaction is catalyzed by GGPP synthase. Subsequently, GGPP is converted to the 40-carbon phytoene by the action of phytoene synthase. The conversion of phytoene to β-carotene involves a series of enzymatic steps, including desaturation, isomerization, and cyclization, mediated by phytoene desaturase (PDS), ζ-carotene isomerase, ζ-carotene desaturase, carotenoid isomerase, and lycopene β-cyclase (LCYB). Following the formation of β-carotene, hydroxylation reactions catalyzed by heme-containing cytochrome P450 enzymes lead to the synthesis of zeaxanthin. Zeaxanthin can be further modified to violaxanthin by zeaxanthin epoxidase (ZEP). Additionally, violaxanthin can be transformed into neoxanthin by violaxanthin de-epoxidase-like (VDL) and further metabolized to vaucheriaxanthin through enzymatic steps that remain to be fully elucidated. To date, only a few enzymes in the carotenoid biosynthesis pathway of \u003cem\u003eN. oceanica\u003c/em\u003e have been characterized \u003cem\u003ein vivo\u003c/em\u003e, including \u003cem\u003ePDS\u003c/em\u003e, \u003cem\u003eLCYB\u003c/em\u003e, \u003cem\u003eZEP\u003c/em\u003e, and \u003cem\u003eVDL\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHere, by establishing a platform that comprises a genome-wide targeted knockout library and high-throughput RACS, we demonstrated an efficient strategy to mine functional genes by efficiently tracing a metabolic trait to its genotypes. From the CRISPR/Cas-generated library of 3,567 \u003cem\u003eN. oceanica\u003c/em\u003e mutants derived from 2,397 effective guide RNAs, label-free sorting for high single-cell carotenoid contents via SCRS rapidly unraveled its underlying genes, including both expected (e.g., \u003cem\u003enoVDEs\u003c/em\u003e) and unexpected (e.g., \u003cem\u003enoPAC4\u003c/em\u003e) ones, which were then validated. These allowed us to propose a multi-level regulation model that reveals new insights into carotenoid biosynthesis in this microalga (\u003cb\u003eFig.\u0026nbsp;8\u003c/b\u003e). (\u003cem\u003ei\u003c/em\u003e) At the transcriptional level, \u003cem\u003enoPAC4\u003c/em\u003e may reduce the expression of \u003cem\u003eHDAC\u003c/em\u003e, thereby influencing transcript level of carotenoid-synthetic genes through epigenetic mechanisms. (\u003cem\u003eii\u003c/em\u003e) At the post-translational level, \u003cem\u003enoPAC4\u003c/em\u003e regulates the 26S proteasome, which in turn maintains chloroplast proteostasis\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and influences degradation of carotenogenic enzymes such as \u003cem\u003ePSY\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, thereby affecting carotenoid accumulation. (\u003cem\u003eiii\u003c/em\u003e) Carotenoid catabolic enzymes such as \u003cem\u003enoVDEs\u003c/em\u003e convert the carotenoid violaxanthin into antheraxanthin and zeaxanthin, as evidenced by the \u003cem\u003enoVDE\u003c/em\u003e-knockout induced 43.3% increase in violaxanthin content. Moreover, reducing retinol synthesis by inhibiting \u003cem\u003eRDH\u003c/em\u003e can also raise carotenoid level.\u003c/p\u003e\u003cp\u003eStudies of microalgal carotenoid metabolism have mainly focused on key enzymes such as \u003cem\u003ePDS\u003c/em\u003e, \u003cem\u003eLCYB\u003c/em\u003e, \u003cem\u003eZEP\u003c/em\u003e, and \u003cem\u003eVDL\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Here we identified \u003cem\u003enoVDEs\u003c/em\u003e as an important player, and showed that knockout of five \u003cem\u003enoVDE\u003c/em\u003e genes in a single transformation resulted in 43.3% increase of violaxanthin content in \u003cem\u003eN. oceanica\u003c/em\u003e, which provides an effective strategy for violaxanthin overproduction. For regulatory mechanisms, reactive oxygen species (ROS), nitric oxide (NO), and calcium ions (Ca\u003csup\u003e2+\u003c/sup\u003e) were found as signaling molecules under salinity or light stresses\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. In contrast, we revealed a post-translational mechanism that modulates carotenoid levels involving proteasome assembly control through PAC. These findings uncovered a new strategy for engineering carotenoid production, while extended the impact of the 26S proteasome-mediated protein degradation from protein misfolding-related disorders such as neurodegenerative diseases\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e to the unexpected realms of plant pigment synthesis and photosynthesis.\u003c/p\u003e\u003cp\u003eThese discoveries underscore the power of screening a genome-wide targeted knockout mutant library, which is not dependent on \u003cem\u003ea priori\u003c/em\u003e knowledge or hypotheses about the underlying genotype. Notably, such mutant libraries have not been reported for any microalgae before, due to their relatively low transformation or genome-editing efficiency\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. To tackle this challenge, we devised a tRNA-based CRISPR/Cas system for \u003cem\u003eN. oceanica\u003c/em\u003e that simplifies the gRNA design and increases the accuracy of ligation for plasmid library construction. Compared to the hammerhead-based CRISPR/Cas system\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, the tRNA-based system eliminates the complex design of the hammerhead structure as well as the spontaneous formation of the stem loop structure, thereby streamlining plasmid construction. This library offers key advantages over those generated by random insertion, chemical mutagens or physical mutagenesis\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. (\u003cem\u003ei\u003c/em\u003e) It enabled rapid and low-cost identification of mutation sites by employing gRNAs as markers to analyze target site mutations. (\u003cem\u003eii\u003c/em\u003e) It elevates the proportion of exon mutations: all gRNAs were targeted to such coding regions, in contrast to that only 40% of genes were of exon mutation in a random insertion based \u003cem\u003eC. reinhardtii\u003c/em\u003e mutant library\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. (\u003cem\u003eiii\u003c/em\u003e) Its genome-wide targeted knockout feature allowed unambiguous and efficient mining of genotypes that underlie a target phenotype by gRNA enrichment, which however is particularly challenging for random mutagenesis libraries due to the presence of multiple mutation sites in a mutant genome\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn screening such large mutant libraries for intracellular metabolites, throughput has long been the primary bottleneck, because the classical yet time-consuming, tedious and costly strategy of agar plating, colony picking, culture for sufficient clonal biomass, biomass lysis and profiling cell extracts for metabolite content still dominates both large-scale bio-foundry facilities and small labs. This process is particularly painful for autotrophic eukaryotic microalgae due to their typically slower growth than bacterial and fungal chasses\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Moreover, many mutants may fail to proliferate during the culture, leading to the loss of crucial genes\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. FACS can greatly accelerate this process by sorting individual cells\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e,yet its application is limited by the narrow scope of metabolites that can be labeled, the interference of autofluorescence, and the often very demanding process of introducing the label either biochemically or genetically. For example, as the basic structural units of photosynthetic apparatus that harvest light for photosynthesis, quench the energy excess under high-light stress and prevent the formation of highly reactive singlet oxygen\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, carotenoids cannot be directly screened by FACS due to the interference of chlorophyll fluorescence. In fact, current quantification methods for carotenoids including near-infrared reflectance spectroscopy (NIRS), visible spectrometry, laser photoacoustic spectroscopy (PAS), UV-visible spectrometry, high-performance liquid chromatography (HPLC) and mass spectrometry (MS) all require carotenoid extraction from a significant amount of bulk biomass\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo tackle this challenge, we established a microfluidics-based, label-free, non-destructive, high-throughput approach for quantifying and then sorting of single-cell carotenoid contents directly from mutant mixtures, based on RACS. We showed that a spontaneous SCRS can accurately quantify contents of total carotenoids at single-cell resolution. Moreover, such contents were sorted at a high throughput of ~ 600 cells min\u003csup\u003e− 1\u003c/sup\u003e, so the top-performing 1% of cells can be genotyped for enriched gRNA sequences to trace the phenotype to underlying genotypes. For instance, \u003cem\u003enoPAC4\u003c/em\u003e mutants with increased violaxanthin, zeaxanthin, and β-carotene levels were obtained in just five hours of a FlowRACS run. By label-freely and non-destructively sorting individual cells for single-cell carotenoid content, the Raman cytometry avoids the tedious fluorescence labeling, saves the arduous time and efforts for agar plating, colony picking and biomass extraction, and minimizes the potential loss of slow-growing mutants during the various stages of cultivation.\u003c/p\u003e\u003cp\u003eBesides carotenoids, a spontaneous SCRS can depict other high-value pigments (e.g., astaxanthin), lipids (e.g., TAG and DHA) and polysaccharides (e.g., starch), as well as their interconversion in the cell\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Moreover, key traits of cell factories, such as viability, vitality, assimilation rate of specific substrates, susceptibility and response to stresses can be profiled by inserting a stable-isotope probing step before SCRS acquisition and sorting\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Therefore, one direction is to take advantage of such rich and diverse single-cell metabolic phenome encoded in a SCRS for single-trait or multi-trait gene mining at a systematic fashion, from the genome-wide \u003cem\u003eN. oceanica\u003c/em\u003e mutant library here or even directly from an environmental microbiome sample. On the other hand, due to signal overlaying of metabolites, SCRS is often limited by difficulty in distinguishing chemical species within a metabolite class or detecting low-level metabolites. To deconvolute SCRS, a RACS-coupled mass spectrometry workflow that supports machine learning of SCRS and the matching population-level metabolomes can be established. Collectively, by label-free sorting genome-wide targeted mutant libraries for single-cell metabolic features, such a forward-genetics approach would greatly accelerate the discovery of new genes and pathways, in not just microalgae but bacterial, fungal, plant and mammalian cells.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStrains and growth conditions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eNannochloropsis oceanica\u003c/em\u003e strains were cultivated in modified f/2 liquid medium\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, which containing 35 g L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e sea salt, 1,000 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e NaNO\u003csub\u003e3\u003c/sub\u003e, 66.6 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e•H\u003csub\u003e2\u003c/sub\u003eO, 3.65 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e FeCl\u003csub\u003e3\u003c/sub\u003e•6H\u003csub\u003e2\u003c/sub\u003eO, 4.37 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Na\u003csub\u003e2\u003c/sub\u003eEDTA•2H\u003csub\u003e2\u003c/sub\u003eO, 0.0196 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e CuSO\u003csub\u003e4\u003c/sub\u003e•5H\u003csub\u003e2\u003c/sub\u003eO, 0.0126 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Na\u003csub\u003e2\u003c/sub\u003eMoO\u003csub\u003e4\u003c/sub\u003e•2H\u003csub\u003e2\u003c/sub\u003eO, 0.044 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e ZnSO\u003csub\u003e4\u003c/sub\u003e•7H\u003csub\u003e2\u003c/sub\u003eO, 0.0109 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e CoCl\u003csub\u003e2\u003c/sub\u003e•6H\u003csub\u003e2\u003c/sub\u003eO, 0.036 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e MnCl\u003csub\u003e2\u003c/sub\u003e•4H\u003csub\u003e2\u003c/sub\u003eO, 5 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e VB\u003csub\u003e12\u003c/sub\u003e, 5 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e biotin, and 0.1 mg L\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e thiamine HCl. Continuous illumination (50 µmol photons m\u003csup\u003e–\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e s\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) at 25°C was applied. For high-light induction conditions (300 µmol photons m\u003csup\u003e–\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e s\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e), the cells were harvested after a 5-day culture period by centrifugation (3,500 g for 5 min), and then resuspended in fresh f/2 medium to achieve a cell density of OD750 = 1. Cell growth was determined based on biomass dry weight and optical density (OD750).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic transformation of the wild-type\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCRISPR/Cas plasmids were introduced into \u003cem\u003eN. oceanica\u003c/em\u003e cells as circular DNA by electroporation, as described\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Briefly, wild-type \u003cem\u003eN. oceanica\u003c/em\u003e cells were cultured in 100 mL to the logarithmic growth phase and then harvested by centrifugation at 5000 \u003cem\u003eg\u003c/em\u003e for 5 minutes at 4°C. The cells underwent two rounds of wash with 25 mL of 375 mM sorbitol at 4°C. For each transformation, 200 µL of cells were combined with 2 µg of the cassette in an electroporation cuvette with a 2 mm gap before electroporation. The electroporation was carried out using a BTX ECM 630 system with a 1,100 V cm\u003csup\u003e− 1\u003c/sup\u003e field strength, 50 µF capacitance, and 600 Ohm shunt resistance. The cells were promptly mixed with 5 mL of f/2 medium and incubated under low light conditions at 120 rpm for 48 hours. Subsequently, the cells were plated on f/2 agar plates supplemented with 5 mg L\u003csup\u003e− 1\u003c/sup\u003e zeocin and 1.6 g L\u003csup\u003e− 1\u003c/sup\u003e NaHCO\u003csub\u003e3\u003c/sub\u003e. Colonies emerged around three weeks later and were typically transferred after 25 days.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCRISPR/Cas library construction and genetic screening strategy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll gRNA candidates for each protein-coding gene were predicted by a local version of CHOPCHOP. The parameters used were “-t CODING -T 1 -g 20 -BB AGGCTAGTCCGT -M NGG -P -J -3 'PRODUCT_SIZE_MIN = 250, PRODUCT_SIZE_MAX = 350, PRIMER_MIN_SIZE = 18, PRIMER_MAX_SIZE = 25, PRIMER_OPT_SIZE = 22, PRIMER_MIN_TM = 57, PRIMER_MAX_TM = 63, PRIMER_OPT_TM = 60' -a 60 -scoringMethod DOENCH_2016 -filterGCmin 30 -filterGCmax 70 -filterSelfCompMax 1 -p 0”. Then, gRNA candidates meeting specific requirements were selected for synthesis. These criteria include: (\u003cem\u003ei\u003c/em\u003e) absence of self-complementarity, (\u003cem\u003eii\u003c/em\u003e) absence of gRNA off-targets with mismatches ranging from 0 to 3 bases, (\u003cem\u003eiii\u003c/em\u003e) predicted efficiencies greater than 50%; and (\u003cem\u003eiv\u003c/em\u003e) target genic sites located within the initial third of the CDS region. If no suitable gRNAs meeting above criteria were found, the last criterion was adjusted to permit targeting sites within the first half of the CDS region. Subsequently, the gRNA library was synthesized by custom microarrays, amplified (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), and then ligated to the digested plasmid with the CRISPR/Cas system by infusion. The ligated plasmids were then transformed into \u003cem\u003eE. coli\u003c/em\u003e, and over 30,000 colonies were selected to establish a plasmid library. The gRNA PCR library and the plasmid library were sequenced by NGS to evaluate their quality. The plasmid library was then transformed into \u003cem\u003eN. oceanica\u003c/em\u003e, with the resulting transformants being transferred into a 24-well plate for subsequent analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHigh-throughput detection of mutation sites in the transformants by NGS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo detect mutations induced by the CRISPR/Cas system, transformants in each 24-well plate were combined to perform shotgun genome sequencing. A tool (scmli; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gongyh/scmli\u003c/span\u003e\u003cspan address=\"https://github.com/gongyh/scmli\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was developed to call the variants around gRNA-targeted sites. Raw reads were quality controlled using Trim Galore\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e with parameters “--paired -q 20 --trim-n --max_n 0 --length 70”. A modified version of snippy (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gongyh/scmli/blob/main/libs/snippy\u003c/span\u003e\u003cspan address=\"https://github.com/gongyh/scmli/blob/main/libs/snippy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; original version: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/snippy\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/snippy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to call preliminary SNPs and InDels with parameters “--basequal 30 --minqual 0.0 --minfrac 0.0”. Finally, variants located within or overlapping with the gRNA target regions, defined as -11 bp to + 5 bp relative to the theoretical cutting site, were retained for subsequent analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSingle-cell sorting of carotenoid-overproducing mutants by FlowRACS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe RACS process was conducted using a FlowRACS 3.0 instrument (Qingdao Single-cell Biotech, Qingdao, China) for \u003cem\u003eN. oceanica\u003c/em\u003e mutant libraries and wild-type controls\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The FlowRACS instrument was equipped with a Nd:YAG 532 nm laser emitter as the excitation light source (20 mW was used in all the experiments), a 50× water objective (NA = 1.0, Olympus, JP) to focus the laser beam on the sample, and an electron-multiplying charge-coupled device (EMCCD) to collect SCRS. The cells were centrifuged at 2000 g and washed three times with sterile deionized water, collected and resuspended in 1% Pluronic F-127 (Sigma Aldrich, USA) for RACS. An alternating current (ac) of 18 volts peak to peak (Vp-p) at 1 MHz frequencies was applied to generate positive dielectrophoresis (pDEP) force to trap the cells, and the 532 nm laser power of 20 mW and acquisition time of 100 ms was used for obtaining SCRS.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMDA amplification of CRISPR/Cas plasmids in the SCRS-based sorted cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe CRISPR/Cas plasmids in FlowRACS-screened \u003cem\u003eN. oceanica\u003c/em\u003e mutants underwent amplification (MDA) with phi29 DNA polymerase\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Approx. 800 cells were sorted using FlowRACS 3.0 with a microfluidics chip (SCB-D002). The sorted cells were lysed with 1 µL lysis buffer (Buffer A) at 65°C for 10 min, followed by the addition of 1 µL stop solution (Buffer B). Subsequently, a 10× HotJa-reaction buffer (0.4 M Tris-HC1 (pH 7.6), 0.5 M KCl, 50 mM (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, 100 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 40 mM DTT), dNTP (0.6 mM each), MDA-F1/F2/F3 primers (3 mM each), and HotJa Phi29 DNA Polymerase were added to each tube. The mixtures were thoroughly combined and incubated at 40°C for 8 hours with a heated lid at 70°C on a thermocycler. The amplified DNA served as the PCR template for amplifying gRNA target sites, and the resulting PCR products were sequenced using NGS.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnrichment analysis of gRNA sequences in the sorted genome-edited\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e \u003cb\u003emutants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenomic RNAs were detected and quantified using the scmli package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gongyh/scmli\u003c/span\u003e\u003cspan address=\"https://github.com/gongyh/scmli\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Significantly enriched gRNAs for each mutant library were identified using edgeR\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, with a false discovery rate (FDR) of \u0026lt; 0.05 and a fold change of greater than 2. Gene Ontology (GO) and KEGG Orthology (KO) terms for the genes targeted by these gRNAs were retrieved from the NanDeSyn database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nandesyn.single-cell.cn\u003c/span\u003e\u003cspan address=\"https://nandesyn.single-cell.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and enrichment analysis was performed using the clusterProfiler package\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of the tRNA-based CRISPR/Cas plasmids for mutating the five\u003c/b\u003e \u003cb\u003enoVDE\u003c/b\u003e\u003cb\u003es in\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo construct five knockout CRISPR/Cas systems for \u003cem\u003enoVDE\u003c/em\u003es, gRNA-HDV was amplified from pNOC-ARS-CRISPR-v2\u003csup\u003e31\u003c/sup\u003e, and glycine tRNA was amplified from the genome of \u003cem\u003eN. oceanica\u003c/em\u003e to create a gRNA-HDV-tRNA structure by infusion. Four pairs of primers (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) were used to amplify the gRNA-HDV-tRNA structure, and the PCR products were ligated to the \u003cem\u003eBsp\u003c/em\u003eQ I-digested tRNA-based CRISPR/Cas backbone by infusion to generate the plasmid for transformation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurement of photosynthesis parameters for the wild-type and mutant\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCells were cultured in flasks to reach the logarithmic phase at 23°C under continuous light (50 µmol photons m\u003csup\u003e− 2\u003c/sup\u003e s\u003csup\u003e− 1\u003c/sup\u003e). Chlorophyll fluorescence of WT and mutants was measured using a pulse-amplitude modulated fluorometer (Image PAM, Walz, Effeltrich, Germany) after 20 min dark treatment of the cells. The maximum quantum yield of photosystem II (Fv/Fm) was measured as previously described\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExtraction and determination of pigments for the wild-type and mutant\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN. oceanica\u003c/em\u003e cells were collected via centrifugation, rinsed with deionized water, and extracted using ethanol. The ethanol extracts were then separated using a Thermo Scientific Vanquish high-performance liquid chromatography (HPLC) system equipped with a variable wavelength detector set at 450 nm for carotenoid detection and an Agilent 5 µm Eclipse XDB-C18 4.6 × 250 mm analytical column. Pigments were eluted at a flow rate of 1 mL min\u003csup\u003e–\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e with a linear gradient from 100% Solvent A (85% methanol) to 70% Solvent B (ethyl acetate) over a 16 min period, followed by 2 min 70% Solvent B. Then pigments were eluted using a linear gradient from 30% Solvent A to 100% Solvent A over 2 min, followed by 10 min of Solvent A. Individual carotenoids were identified by their absorption spectra, and their typical retention times were compared to standard samples of pure carotenoids.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA-Seq library construction for the wild-type and mutant\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTotal RNA of \u003cem\u003enoPAC4\u003c/em\u003e mutants and WT cultured for five days was extracted using Trizol reagents (Tiangen, China). Concentration and purity of the RNA were determined spectrophotometrically (IMPLEN, USA), and RNA integrity was assessed with the RNA Nano 6000 Assay Kit on an Agilent Bioanalyzer 2100. A total of 2 µg of RNA per sample was used as input material for RNA sample preparation. Sequencing libraries were generated using the NEBNext Ultra™ RNA Library Prep Kit for Illumina (NEB, USA), and indices were added to associate sequences with each sample. The clustering of indexed samples was performed on a cBot Cluster Generation System using the HiSeq 3000/4000 PE Cluster Kit Box 1 (Illumina, USA). After cluster generation, the library preparations were sequenced on an Illumina HiSeq 4000, generating 150 bp paired-end reads.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComputational analysis of transcriptome data from the wild-type and mutant\u003c/b\u003e \u003cb\u003eN. oceanica\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRNA-seq datasets underwent quality control and then generation of gene expression profiles, based on the nfcore/rnaseq pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nf-co.re/rnaseq\u003c/span\u003e\u003cspan address=\"https://nf-co.re/rnaseq\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Specifically, reads were aligned with STAR, employing modified parameters to limit intron lengths (“--alignIntronMin 20, --alignIntronMax 3000”). Subsequently, Trinity\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e was utilized to generate a gene expression matrix using the RSEM2 method, with TMM normalization implemented to account for library size variations among samples. Then transcripts per million (TPM) values were averaged across replicates. For the mutant samples, DEGs were identified by edgeR\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, via an FDR threshold of ≤ 0.001 and an FC of \u0026gt; 2. Statistical significance of TPMs across different categories was assessed by the Wilcoxon test. Gene set enrichment for GO terms and KEGG pathways was analyzed by clusterProfiler\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, with a Benjamini-Hochberg adjusted \u003cem\u003ep\u003c/em\u003e value of 0.05 set as the significance cutoff.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll sequence data were deposited to the NCBI Sequence Read Archive (PRJNA1188048 for genomic data) and Gene Expression Omnibus (GSE281748 for transcriptomic data). To facilitate validation and foster collaboration, all gRNAs validated in this study were deposited into the NanDeSyn database (https://nandesyn.single-cell.cn/CRISPR_library), and are freely available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCodes for detecting gRNAs and mutation sites are available via GitHub at https://github.com/gongyh/scmli.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJX, BM, QTW, and YHG designed the study. QTW, XFD, LHW, and NNL constructed the CRISPR/Cas mutant library. XXW and BM performed the FlowRACS. XFD and LHW phenotypes the mutants. YHG and JSZ analyzed the NGS data. ZN and YL participated in data interpretation and manuscript editing. JX, QTW, YHG and XXW wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Key Research and Development Program of China (No. 2021YFA0909700), National Natural Science Foundation of China (No. 32270109, 3217010815 and 32470087) and the CAS-CEA Partnership Program. We are also grateful to NaturalScience Foundation of Shandong Province (2021ZDSYS29 and ZR2024YQ048), Science and Technology Benefiting People Program of Qingdao (No. 23-2-8-smjk-3-nsh), CAS Young Scientists in Basic Research Program (No. YSBR-111), CAS Young-Scientist Innovation Promotion Association, and Shandong Taishan Scholarship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJX and BM are on the scientific board of Qingdao Single-cell Biotechnology Co. No other competing interest is declared\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFalkowski, P.G.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The evolution of modern eukaryotic phytoplankton. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e305\u003c/strong\u003e, 354-60 (2004).\u003c/li\u003e\n \u003cli\u003eKhan, M.I., Shin, J.H. \u0026amp; Kim, J.D. The promising future of microalgae: current status, challenges, and optimization of a sustainable and renewable industry for biofuels, feed, and other products. \u003cem\u003eMicrob. Cell Fact.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 36 (2018).\u003c/li\u003e\n \u003cli\u003eBarkia, I., Saari, N. \u0026amp; Manning, S.R. Microalgae for high-value products towards human health and nutrition. \u003cem\u003eMar. 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Using the industrial microalga \u003cem\u003eNannochloropsis oceanica\u003c/em\u003e as a model, we introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated Cell Sorting (RACS). The\u003cem\u003e \u003c/em\u003eCRISPR/Cas-generated library consists of 3,567 microalgal mutants derived from 2,397 effective guide RNAs. Label-free sorting of the library for high carotenoid content by RACS unravels mutations in the violaxanthin de-epoxidase (\u003cem\u003enoVDE\u003c/em\u003e) or in the proteasome assembly chaperone 4 (\u003cem\u003enoPAC4\u003c/em\u003e) genes. Knocking out all five known \u003cem\u003enoVDE\u003c/em\u003es reveal that the high carotenoid content is due to violaxanthin increase, whilst \u003cem\u003enoPAC4\u003c/em\u003e knockout boosted carotenoid content with elevations in violaxanthin, zeaxanthin, and β-carotene. Genetic and transcriptomic evidences suggest two previously unknown modes of carotenogenesis regulation mediated by noPAC4: epigenetic mechanisms via histone deacetylase (HDAC) and post-translational controls by the 26S proteasome. Therefore, by label-freely sorting single-cell metabolic phenotype and rapidly yet unambiguously tracing it to a genotype, this new forward-genetics approach can greatly accelerate the discovery of new genes and pathways.\u003c/p\u003e","manuscriptTitle":"High-throughput Raman-activated cell sorting of microalgal genome-wide edited library revealed a new regulatory pathway for carotenoid synthesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 06:38:27","doi":"10.21203/rs.3.rs-7026720/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6655f863-f2a7-4571-8a10-010f97057d44","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52088907,"name":"Biological sciences/Genetics/Functional genomics/Mutagenesis"},{"id":52088908,"name":"Biological sciences/Molecular biology/Transcriptomics"},{"id":52088909,"name":"Biological sciences/Biological techniques/Genetic engineering"},{"id":52088910,"name":"Biological sciences/Biological techniques/High-throughput screening"}],"tags":[],"updatedAt":"2025-07-29T06:38:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 06:38:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7026720","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7026720","identity":"rs-7026720","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}