In the study of plant gene function, genetic redundancy has long stood as a central, persistent challenge. A vast number of plant genes exist in the form of gene families; consequently, single-gene mutations often fail to produce discernible phenotypes, making it difficult for traditional mutant screening methods to reveal true biological functions.
CRISPR technology offers new possibilities for addressing this issue; however, current large-scale plant CRISPR libraries remain subject to three key limitations: a lack of spatial control, a tendency to include only a single sgRNA per construct, and the difficulty of systematically addressing the redundancy inherent in gene families. Consequently, the ability to simultaneously achieve multi-gene targeting, tissue-specific editing, and a traceable library screening system within the context of large-scale genetic screens has emerged as a critical technical bottleneck in the field of plant functional genomics.
On March 9, 2026, Cell Reports published online the latest research findings from the team led by Eilon Shani and Itay Mayrose at Tel Aviv University, titled: "Targeting redundant gene families: A multiplexed, tissue-specific CRISPR toolbox for Arabidopsis genetic screens." This study describes the construction of a multiplexed, tissue-specific CRISPR library system designed specifically for functional genomics screens in plants. The system features an integrated design across three distinct levels: First, improved sgRNA design algorithms were developed to generate sgRNAs capable of simultaneously targeting multiple members of a single gene family. Second, a multiplex editing strategy was implemented by co-expressing two distinct sgRNAs within a single vector. Third, a dual-barcode CRISPR-GuideMap system was introduced, enabling the precise tracking of the specific sgRNA combinations carried by each individual plant within a large-scale transgenic population.
Figure 1. Overview of the next-generation CRISPR genetic toolbox. (Anfang, et al. 2026)
As a case study, the research team applied this system to target plant nutrient transporter genes in Arabidopsis, constructing and screening a population of over 1,000 transgenic plants. The results demonstrated that the multiplexed sgRNA library significantly enhanced both gene coverage and editing efficiency, boosting the gene editing rate from 48.4%—typical of single-sgRNA libraries—to 84.2%. Furthermore, the system successfully recapitulated several previously characterized phenotypes while simultaneously revealing multiple novel phenotypes that had previously remained hidden due to the masking effects of genetic redundancy.
Specifically, the authors focused their investigation on how to systematically target redundant gene families within the context of large-scale CRISPR libraries.
The research team first screened for transporter protein genes that were differentially expressed under seven nutrient-deficient conditions (N, K, P, Ca, Fe, Mg, and S), yielding a total of 452 candidate transporter genes. Subsequently, these genes—along with their homologous members—were expanded into a set of 707 target genes to serve as the target collection for the CRISPR library.
Utilizing the CRISPys algorithm, the team designed sgRNAs capable of simultaneously targeting multiple members within the same gene family. Compared to the previously developed Multi-Knock library, this study employed the MOFF deep-learning scoring model to predict Cas9 editing efficiency; furthermore, it filtered out sgRNAs targeting sparsely related members of a given gene family, thereby enhancing the probability of sgRNAs successfully targeting functionally redundant members.
The final constructed library comprises 2,859 sgRNAs, with each sgRNA targeting an average of 2.32 genes. These sgRNAs were categorized into seven sub-libraries based on nutrient type; for instance, the sub-library for nitrogen deficiency-related sgRNAs contains 1,805 entries, while the potassium-related sub-library contains 1,314 entries.
Deep sequencing results revealed that the sgRNA coverage across the seven sub-libraries ranged from 99.48% to 100%, exhibiting a narrow-peak distribution that indicates the high quality of the library construction.
Next, the authors constructed a set of tissue-specific CRISPR vectors, utilizing various tissue-specific promoters to drive Cas9 expression. Examples include: pARSK1 (roots), pKST1 (guard cells), pSCR (endodermis), pSUC2 (phloem companion cells), and pPGP4 (root epidermis and cortex).
The vector design incorporates an intronized Cas9 (containing 13 introns)—a design previously demonstrated to significantly enhance editing efficiency in Arabidopsis.
As a validation step, the authors placed the iron transporter sgRNA sub-library under the control of the pPGP4 promoter, thereby directing mutations to occur primarily within the root epidermal and cortical cells. Deep sequencing confirmed an sgRNA coverage rate approaching 100%.
During the transformation screening process, the authors identified a plant exhibiting an iron-deficiency-like phenotype. Sequencing analysis revealed that the sgRNA simultaneously targeted both IRT1 and IRT2; furthermore, the observed phenotype was consistent with that of known irt1 mutants, suggesting that IRT1 acts predominantly from the root epidermis and that the activities of IRT1 and IRT2 are not redundant.
In addition, the screen recovered a line targeting SULTR1;1 and SULTR1;2 that exhibited significant shoot growth inhibition and reduced sulfate levels, thereby validating the functional redundancy between these two sulfate transporters. Sequencing results further demonstrated that mutations occurred specifically in root tissues but not in shoots, confirming the efficacy of tissue-specific editing.
Figure 2. Cell-type-specific multi-targeted CRISPR library screen identifies putative iron transporters. (Anfang, et al. 2026)
The core question the authors next sought to address was whether the editing efficiency for gene families within large-scale libraries could be enhanced through the use of multiplex sgRNAs.
The research team designed a novel multiplex library architecture in which each vector carries two distinct sgRNAs. Through systematic algorithmic optimization, the sgRNAs within each vector were configured to target different combinations of members within the same gene family. The resulting library comprised 1,599 multiplex constructs, incorporating 1,826 sgRNAs and targeting a total of 694 genes.
Compared to a single-sgRNA library, the multiplex library demonstrated significant improvements: the average number of targeted genes per vector increased from 2.32 to 3.03, while the average number of mismatches decreased from 0.706 to 0.347 per target. These enhancements collectively boosted both targeting efficiency and gene coverage.
In editing efficiency assays, the multiplex library achieved an average editing efficiency of 84.2%, a figure significantly higher than the 48.4% observed in the single-sgRNA library. Notably, when two sgRNAs simultaneously targeted the same gene, the editing efficiency reached 100%.
In the context of large-scale CRISPR library screening, another critical challenge lies in accurately identifying which specific sgRNA—or combination of sgRNAs—is carried by each individual plant.
To resolve this issue, the authors developed the CRISPR-GuideMap barcode system. This method involves incorporating a unique barcode into each specific sgRNA combination; subsequently, deep sequencing can be utilized to precisely identify the sgRNA combination present in any given plant specimen. Among the 1,014 T1 plants analyzed: 73% had their sgRNA combinations successfully identified; 17% contained two independent transformation insertions; and 10% could not be resolved due to sequencing noise. On average, each gene was targeted in 4.16 individual plants.
This strategy enables researchers to directly retrieve mutants of specific gene families—such as multi-gene mutant combinations within the PUP cytokinin transporter family—directly from a seed library.
Utilizing this library, the authors successfully recovered multiple previously characterized phenotypes associated with specific gene mutations during their screening process.
For instance, a triple mutation in HMA2/HMA3/HMA4 resulted in significantly stunted plant growth, consistent with the phenotype previously reported for the hma2 hma4 double mutant. Mutations in AAC2 and ER-ANT1 produced small, pale plants. Furthermore, a triple mutation in PIN3/PIN4/PIN7 led to distinct alterations in plant growth and leaf morphology.
In addition, the library revealed previously unreported phenotypes resulting from functional redundancy. For example, mutant line #762—in which the four transporter genes SWEET11–14 were simultaneously knocked out—exhibited smaller shoots and significantly reduced leaf area.
Further analysis demonstrated that SWEET13 and SWEET14 transport the cytokinin derivative trans-zeatin (tZ), whereas SWEET11 and SWEET12 do not. However, biochemical characterization indicates that this phenotype cannot be solely attributed to impaired cytokinin transport, suggesting that functional divergence among SWEET family members and other mechanisms collectively contribute to the observed phenotype.
In summary, this study demonstrates that multi-gene CRISPR screening can uncover biological functions that are otherwise masked by functional redundancy within gene families. The use of a multiplex sgRNA library significantly enhances the capacity for targeting entire gene families. Moreover, the CRISPR-GuideMap barcoding strategy effectively resolves the challenge of tracking specific sgRNAs within large-scale plant CRISPR libraries.
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