Oil palm is the most productive oilseed crop and its oil yield is seriously affected by frequent drought stress. However, little is known about the molecular responses of oil palm to drought stress. We studied the root transcriptomic responses of oil palm seedlings under 14-day drought stress. We identified 1293 differentially expressed genes (DEGs), involved in several molecular processes, including cell wall biogenesis and functions, phenylpropanoid biosynthesis and metabolisms, ion transport and homeostasis and cellular ketone metabolic process, as well as small molecule biosynthetic process. DEGs were significantly enriched into two categories: hormone regulation and metabolism, as well as ABC transporters. In addition, three protein–protein interaction networks: ion transport, reactive nitrogen species metabolic process and nitrate assimilation, were identified to be involved in drought stress responses. Finally, 96 differentially expressed transcription factors were detected to be associated with drought stress responses, which were classified into 28 families. These results provide not only novel insights into drought stress responses, but also valuable genomic resources to improve drought tolerance of oil palm by both genetic modification and selective breeding. Elaeis guineensis, Jacq.) is the most productive oilseed crop1. Palm oil extracted from palm fruits has been used as cooking oil and in making cosmetics, candles, soaps, biofuels, and lubricating greases, as well in processing tinplate and coating iron plates, and the demand for palm oil is increasing1. Oil palm is mainly cultivated in the tropical regions of Southeast Asia, Africa and South America3, where water is particularly important to the productivity of this crop. Oil palm needs ~ 2000 mm/year rain water to have a normal production and does not tolerate to drought for more than 90 days3. However, with unpredictable global climate changes, tropical regions are accidentally in danger of lacking of rainfall of up to months4. Continuous drought condition affects oil palm production frequently5. A previous study showed that deficit of 100 mm water reduced the yield by 20%6. Recent studies demonstrated that drought stress for 7–21 days induced physiological and growth changes in oil palm seedlings9. However, not much is known about transcriptomic response to drought stress. Therefore, it is essential to increase drought tolerance in oil palm to ensure sustainable development of palm oil industry. Understanding of more about the response of oil palm to drought stress will facilitate the breeding of drought tolerant oil palm varieties for sustainable oil production11.Oil palm (Arabidopsis) and have identified important genes and/or evolutionary conserved genetic elements that act to regulate drought tolerance14. Drought resistance is complex and determined by genetic and environmental factors, and their interactions. It takes very long time to increase drought tolerance by conventional breeding15. The mechanisms underlying drought tolerance are variable among different species18 and are still poorly understood for most non-model species19. Thus, identifying the genetic factors and understanding the underlying mechanisms are essential to improve drought tolerance by gene modifications20 and breeding5 in different species.An increasing number of studies have focused on understanding the molecular mechanisms of drought tolerance in agronomic plant species or model plant species , which works as an important phytohormone, has well-known functions in drought tolerance in plants, by inducing the expression of stress-related genes25. Another study also indicated that jasmonic acid (JA) was associated with stomatal closure to increase drought tolerance26. In addition, it was reported that transcription factors (TFs), including NAC, GmNAC, HD-STARTtype and NF-YB family members, played important roles in drought tolerance by regulation of hormone metabolisms27. Beyond individual genes, cascaded signalling pathways and regulatory networks were suggested to have played indispensable roles in drought tolerance28. ABA coupled with corresponding TFs and ABA responsive elements, could regulate the expressions of a wide range of genes under osmotic stress, via cis regulations28. A number of protein families in the calcium signalling pathways, mitogen-activated protein kinases (MAPKs) signalling pathways and phosphorylation cascades were also involved in drought stress responses30. Previous studies on the responses of plants to drought stress have shed new insights in understanding of the mechanisms of drought tolerance in different species. However, in the oil palm, transcriptomic responses to drought stress are still poorly understood.Plants may use diverse mechanisms to maintain water and ion homeostasis31. Herein, oil palm seedlings were firstly treated with drought stress. We studied the transcriptome responses of roots to drought in the primary tissue for stress signal perception and initiating of cascade gene regulation pathways in response to drought. A total of 1293 DEGs, including 96 transcription factors, were identified in drought stress responses. Besides individual genes, signalling pathways and protein–protein interaction networks, involving transcription factors, also likely have played crucial roles in drought tolerance. This study provides both novel insights of molecular response of oil palm to drought stress and genomic resources to improve and develop drought-tolerant oil palms for sustainable oil production.The purpose of the current study was to identify genes, pathways, networks and transcription factors involved in drought response using RNA-seq and bioinformatics analysis to understand more about molecular responses of oil palm roots under drought stress. RNA-seq is a next-generation sequencing technology used to analyse the presence and quantity of RNA molecules in biological samples9 and other plant species34 under drought stresses and undergoing water deprivation. These results indicate substantial physiological responses of the oil palm seedlings under drought stress35, and provided useful starting materials to study the genes, pathways and networks involved in drought responses using RNA-seq36 and bioinformatics analysis.Obvious morphological changes in leaves and roots of oil palm seedlings under drought stress with a period of 14 days were observed. The effects of drought stress were first observed in leaf morphology, showing initial edge and tip necrosis and then wilting and yellowing for the drought treated samples , indicating the duplicated genes also play important roles in response to drought stress38 in oil palm, a species of palaeotetraploid origin37 and future studies should also focus on paralogous genes and their potential functions in stress responses39.An average of 70.2 million (M) cleaned reads out of 71.3 M raw reads, were obtained across all six samples. The controls had higher reads coverage than the drought-stressed palms (84.8 M vs 55.7 M) (Supplementary Table P < 0.0001), as examined using Pearson’s correlation test  and response to osmotic stress and water homeostasis . Differential expressions of genes in these functions likely result from the responses of plants to water deprivation by direct regulation of osmotic pressure43. In addition, a number of genes were enriched into the biological categories related to regulation of cellular ketone metabolic process (GO:0010565), suggesting that genes involved in ketone metabolic process play important roles in drought stress in oil palm45. Hormone regulations are also indispensable to stress responses of plant species. Here, we identified two enriched GO terms related to hormone regulation and metabolism . Previous studies have shown that production of numerous secondary metabolites is essential for physiological processes to respond to abiotic stress47. Consistent with these results, we found several significant enrichments related to these terms: small molecule biosynthetic process (GO:0044283), amino sugar and nucleotide sugar metabolism (ath00520), galactose metabolism (ath00052), benzene-containing compound metabolic process (GO:0042537), linoleic acid metabolism (ath00591) and xyloglucan metabolic process (GO:0010411). Interestingly, we also identified significant enriched GO terms, like response to jasmonic acid (GO:0009753) and ABC transporters (ath02010), which play crucial roles in abiotic stress responses , was a duplicated copy of the up-regulated one, jasmonic acid-amido synthetase JAR1 (LOC105046997), suggesting functional divergence of paralogous genes since genome duplication events. Nevertheless, consistent with the expression patterns of most DEGs in this subcategory, the up-regulated jasmonic acid-amido synthetase JAR1 might be more important in regulation of drought stress response in oil palm. Moreover, we found that all of the DEGs, including probable protein phosphatase 2C 24 and two duplicated copies of probable protein phosphatase 2C 75, in the subcategory carotenoid biosynthesis, were up-regulated. These three DEGs were also enriched into the subcategory abscisic acid pathway (ABA) within MAPK signalling pathway  were differentially expressed against drought stress. As shown in previous studies, multidrug resistance‐associated proteins are widely involved in regulation of stress responses, such as salt stress, water deprivation, oxidative stress and fungal stress58. Taken together, these different types of ABC transporters likely play important roles in responses to drought stress in oil palm.Membrane transporters play vital roles in regulation of water and ion homeostasis of organisms, among which ATP-binding cassette (ABC) transporters constitute one of the largest protein families and act as both exporters and importers, driven by ATP hydrolysisrs Table . Five of16. Nevertheless, we also observed that three DEGs were down-regulated in the same network, implying both positive and negative feedback regulations are acting on this network59. Reactive nitrogen species metabolic process is also suggested to have critical roles in stress responses, such as drought and salinity60. Consistently, we identified three up-regulated genes: magnesium transporter MRS2-1 (MGT2), putative chloride channel-like protein CLC-g (AT5G33280) and serine/threonine protein kinase OSK1 (KIN10), in this protein–protein interaction network. Nitrate assimilation is another biological process affecting salt and water stress tolerance in plants61. Here, we found four DEGs involved in this network: two were up-regulated , while the other two were down-regulated [amino acid permease 8 (AAP8) and vacuolar cation/proton exchanger 1a (CAX1)]. As these protein–protein interaction networks play crucial roles in drought stress response, the DEGs involved in these networks provide important candidate genes to improve drought tolerance of oil palm by genetic engineering and/or selective breeding.Other than significantly enriched GO terms and KEGG pathways, we also identified three protein–protein interaction networks, focused on ion transport, reactive nitrogen species metabolic process and nitrate assimilation Fig. ; Table 221. Transcription factors are vital for development, response to intercellular and environmental signals and pathogenesis21. The expression changes are often associated with important cellular processes15. In this study, we identified 96 differentially expressed transcription factors that were classified into 28 families  were geminated with a standard protocol53. The seedlings were grown in a nursery for 120 days before drought stress treatment. Eight oil palm Tenera seedlings were planted in pots with diameter of 20 cm containing natural soil with water content of 23% (2.3 g water per 10 g soil), and placed in a greenhouse with a natural tropical temperature ranging from 28 to 34 °C, 30–50% relative humidity and natural photoperiod. Four seedlings were watered twice a week to maintain water content of > 23% while the other four seedlings were used as drought treatment without watering for two weeks. After drought stress challenge of 14 days, the mortality rate of experimental group was estimated at ~ 50%. The root tissues of both the control and experimental groups were harvested and measured, respectively. The samples were then preserved at − 80 °C for RNA isolation.Seeds of Total RNA was isolated from roots using RNeasy Plant Mini Kit , according to the manufacturer’s instructions. RNA quality was assessed by agarose gels and concentration was measured by NanoDrop . One µg total RNA from each sample was firstly treated with RNase-free DNase I  and then used for mRNA library construction with Illumina TruSeq RNA Library Prep Kit v2 , according to the manufacturer’s instructions. The libraries were paired-end sequenced (2 × 75 bp) using an Illumina NextSeq500 . Three biological replicates were sequenced for both control and drought treated samples. For validation of RNA sequencing data using real-time quantitative PCR (qPCR), two µg total RNA was treated with RNase-free DNase I  and was then used for synthesizing cDNA with the MMLV reverse transcriptase .process_shortreads in Stacks package65, to demultiplex samples, filter adaptors and clean up low quality reads. The program STAR66 was employed to align and map the cleaned reads to the reference genome of oil palm37, with default parameters. Only uniquely mapped reads were used to analyse the expression patterns of annotated genes. The program HTSeq-count67 was then used to count the expression level of each annotated gene, based on the information of gene features in the genome annotation file. We used both DESeq268 and EdgeR69 to normalize the relative expression of transcripts across samples. Only transcripts with the number of counts per million (CPM) mapped reads of > 1 were retained for further analysis. Transcripts with a fold change (FC) value of > 2 or < − 2 and with a significance value of 0.01 after application of Benjamini–Hochberg false discovery rate (FDR)70 were considered as differentially expressed genes, between drought treatment and control groups. Only DEGs that were consistently identified by both DESeq2 and EdgeR, were used for further analysis.Raw sequencing reads were processed using the program 71 were retrieved for each DEG, according to the PalmXplore database of oil palm72. We first clustered all the samples using both principal component analysis (PCA) and heatmap approaches with the program ClustVis73, based on the relative expression of DEGs, to investigate the overall expression patterns between drought treatment and control groups. Gene ontology enrichment analysis was carried out using the program Metascape 74. The Metascape program74 was further employed to study the protein–protein interactions using network analysis by referencing to Arabidopsis. The candidate signalling pathways associated DEGs were classified and enriched by annotating against the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database75 of oil palm.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) accessions 76. Both β-actin gene and glyceraldehyde 3-phosphate dehydrogenase gene (GAPDH) were used as housekeeping genes to normalize the relative expression of genes, according to our previous study77. The 2−ΔΔCT method was used to quantify the expression level according to our previous method78. The experiment was carried out with three biological replications, each with three technical replicates.DEGs were randomly selected and the relative expression patterns revealed by RNA-seq were examined by qPCR, to assess the effectiveness and accuracy of the whole DEGs dataset. Primers of randomly selected DEGs were designed according to the coding sequences, obtained from the annotated reference genome, using the program Primer3All authors have reviewed the final version of the manuscript and agree to publish the data.Supplementary Information S1.Supplementary Information S2.


Droplet-based high throughput single cell sequencing techniques tremendously advanced our insight into cell-to-cell heterogeneity. However, those approaches only allow analysis of one extremity of the transcript after short read sequencing. In consequence, information on splicing and sequence heterogeneity is lost. To overcome this limitation, several approaches that use long-read sequencing were introduced recently. Yet, those techniques are limited by low sequencing depth and/or lacking or inaccurate assignment of unique molecular identifiers (UMIs), which are critical for elimination of PCR bias and artifacts. We introduce ScNaUmi-seq, an approach that combines the high throughput of Oxford Nanopore sequencing with an accurate cell barcode and UMI assignment strategy. UMI guided error correction allows to generate high accuracy full length sequence information with the 10x Genomics single cell isolation system at high sequencing depths. We analyzed transcript isoform diversity in embryonic mouse brain and show that ScNaUmi-seq allows defining splicing and SNVs (RNA editing) at a single cell level. Droplet-based high throughput single cell sequencing techniques can often lose information on transcript splicing and heterogenity. Here the authors introduce ScNaUmi-seq, which uses Oxford Nanopore sequencing and barcoding to generate high accuracy full length sequences. While droplet-based high throughput scRNA-seq approaches  allow the analysis of thousands of cells, they only yield limited sequence information close to one extremity of the transcript after Illumina short-read sequencing. Information crucial for an in-depth understanding of cell-to-cell heterogeneity on splicing, chimeric transcripts and sequence diversity  is lacking.Single-cell RNA sequencing (scRNA-seq) is a key technique for the analysis of cell-to-cell heterogeneity and projects aiming at analyzing the transcriptome of all cells from complex organisms have been initiated . Such a low depth limits analysis to highly expressed transcripts. Oxford Nanopore PromethION long-read sequencers generate 20 times more reads per flow cell than the PacBio Sequel II. The first Nanopore sequencing of 10x Genomics single-cell libraries was a targeted sequencing of antigen receptors in lymphocytes4. To assure correct barcode assignment, both studies used high accuracy Illumina sequencing data of the same libraries to guide cell barcode (cellBC) assignment to long reads. Volden et al.5 recently presented a Nanopore sequencing approach with undisclosed barcode assignment accuracy, that does not require Illumina data for cellBC assignment.Long-read sequencing can overcome this limitation. Several studies used Pacific Biosciences (PacBio) or Nanopore long-read sequencing to obtain full-length single-cell data with the 10x Genomics Chromium system. Gupta et al.4 or did not correct UMI sequencing errors5. They assigned every novel UMI read sequence to a novel UMI. However, with this strategy, the higher sequencing error rate of long-read sequencers causes serious issues. PacBio sequencers have a circular consensus error rate of about 1%. In consequence, about 10% of the 10 nt. UMI reads are expected to have at least one error. Fake UMIs are an even more serious issue with the higher Nanopore sequencing error rate of 5–8%. For 45% of the UMIs identified by Nanopore sequencing, Volden et al.5 could not find a corresponding UMI in the Illumina dataset of the same sample. The authors suggest that at least one-third of the UMIs were mis-assigned with their strategy due to Nanopore sequencing errors.Single-cell sequencing library preparation requires rather strong PCR amplification. Amplification bias and chimeric cDNA generated during PCR amplification are issues that can be both addressed by unique molecular identifiers (UMIs), short random sequence tags that are introduced during reverse transcription. UMIs allow grouping of reads that correspond to same RNA molecule and elimination of PCR artifacts. In consequence UMIs minimize the risk that PCR generated chimeric cDNAs are falsely annotated as novel transcripts. Furthermore, UMIs allow the generation of error-corrected consensus sequences for each RNA molecule. Obviously, accuracy of UMI assignment is crucial. Previous long-read single-cell sequencing approaches either did not use UMIsScNaUmi-seq (Single-cell Nanopore sequencing with UMIs), enables the analysis of splicing and sequence variation at the single-cell level with the 10x Genomics Chromium system. This is illustrated with data on alternative splicing and RNA editing in embryonic mouse brain.We addressed those issues and designed a long-read single-cell sequencing approach that combines the high throughput of Nanopore sequencing with high accuracy cellBC and UMI assignment. Our approach, entitled 6 and 70 × 106 Illumina reads  and the polyA-tail of the cDNA  the barcodes detected in the Illumina short-read data. We then compared the cellBC sequence extracted from each genome aligned Nanopore read with the cell barcodes found in the Illumina data for the same gene or genomic region. Following this strategy, we assigned cellBCs to 68 ± 4% of Nanopore reads with identified poly(A) and adapter sequence  and cellBC discovery rates of our approach are, despite the higher error rates of Nanopore reads, similar to those reported previously for PacBio sequencing of 10x Genomics libraries5, we designed a UMI assignment strategy that is guided by Illumina high accuracy sequencing data. After assignment of the cellBC to the Nanopore read, we compared the Nanopore UMI read sequence with the UMI sequences found for the same gene (or genomic region) and the same cell in the Illumina sequencing data (see methods section for details). This strategy drastically reduces the complexity of the UMI search set, which corresponds to the number of transcripts molecules captured for a given gene or genomic region in one cell. Using this strategy, we assigned UMIs to 76 ± 3% of the reads with identified cellBC ) of both cellBC and UMI assignment were 99.8% and 97.4%, respectively  than with the short-read data of the same sample .We next examined the accuracy of our cellBC and UMI assignment strategy. We replaced either the cellBC or the UMI sequence in each Nanopore read by a random sequence and examined the number of cellBC and UMI assigned reads (see methods section for details). The accuracy , UMI counts  and gene expression for individual cells . In consequence, our cellBC and UMI assigned Nanopore dataset represents well the transcriptome captured in the 10x Genomics workflow.To evaluate whether the additional cycles of full-length PCR amplification (see “Methods” section) or Nanopore library preparation skew the long-read dataset, we compared the cellBC and UMI assigned Nanopore data to the Illumina data of the same sample. On average, 79% of the RNA molecules (UMIs) and 91% of the genes identified in each single cell after Illumina sequencing were also found in our Nanopore dataset Fig. . The cel6. While we noticed this for certain genes , we also found many instances of well-expressed genes showing expression of several isoforms in a single cell  allows boosting the Nanopore sequencing accuracy from 93% to beyond 99%  performs in defining SNVs for the ionotropic glutamate receptor Gria2, a post-synaptic cation channel that is A- > I edited at two sites, leading to a Q/R substitution within the pore that renders the channel Caing Fig.  is almosing Fig.  and thating Fig.  from 9.3Combining the high throughput of Nanopore sequencing with UMI guided error correction thus allows both high confidence definition of transcript isoforms and identification of sequence heterogeneity in single cells.3. (ii) This is the first approach that enables accurate UMI assignment to long reads. (iii) The additional short-read dataset of the same sample provides a quality control that allows detecting bias introduced by the additional amplification of full-length cDNA during Nanopore library preparation.Accurate cellBC and in particular UMI assignment is crucial in single-cell sequencing. To achieve this, we opted for an approach that is guided by Illumina data. While this requires additional short-read sequencing, this strategy has multiple advantages. (i) We achieve a cellBC assignment accuracy that is, despite the higher error rate of Nanopore reads, comparable to that previously reported for PacBio readsOne principal advantage of our approach is the accurate UMI assignment. The use of UMIs is even more important for long read than for short-read sequencing. Chimeric cDNA generated during PCR amplification is not an issue in short-read sequencing when only one extremity of the cDNA is sequenced. Conversely, PCR artifacts can severely affect the quality of long-read sequencing data. UMIs allow elimination of most of those PCR artifacts .A combined hippocampus, cortex, and ventricular zone pair from an E18 C57BL/6 mouse was obtained from BrainBits LLC . A single-cell suspension was prepared following the 10x Genomics protocol for “Dissociation of Mouse Embryonic Neural Tissue”. Briefly, tissues were incubated with 2 mg/ml Papain in calcium free Hibernate E medium (BrainBits) for 20 min at 37 °C, rinsed with Hibernate-E/B27/GlutaMAX (HEB) medium, triturated and filtered through a 40 µm FlowMi cell strainer (Sigma-Aldrich). Cell concentration and viability were determined with a CountessThe E18 mouse brain single-cell suspension was converted into a barcoded scRNA-seq library with the 10x Genomics Chromium Single Cell 3′ Library, Gel Bead & Multiplex Kit and Chip Kit (v2), aiming for 1400 cells following the manufacturer’s instructions with the following modifications: To obtain batches with different cell numbers, we split the emulsion before reverse transcription into two aliquots . We extended the PCR elongation time during the initial PCR amplification of the cDNA from the manufacturer recommended 1 min to 3 min to minimize preferential amplification of small cDNAs. Half of the amplified cDNA was used for short-read sequencing library preparation following the 10x Genomics protocol and sequenced on an Illumina Nextseq 500 sequencer (26bases + 57bases). We generated 43 M and 70 M Illumina reads for the samples targeting 200 and 1200 cells respectively and mapped them to the mouse genome (build mm10) with the 10x Genomics Cell Ranger software (v2.0.0).For each PromethION flow cell (Oxford Nanopore) we re-amplified 2–10 ng of the 10x Genomics PCR product for eight cycles with the primers NNNAAGCAGTGGTATCAACGCAGAGTACAT and NNNCTACACGACGCTCTTCCGATCT . The Ns at the 5′ end of the primers avoid the preferential generation of reverse Nanopore reads (>85%) we observed without those random nucleotides. Amplified cDNA was purified with 0.65x SPRISelect  and Nanopore sequencing libraries were prepared with the Oxford Nanopore LSK-109 kit (PCR free) following the manufacturer’s instructions. For the libraries targeting 200 and 1200 cells, we generated 32 M reads and 322 M reads, respectively.Amplified single-cell cDNA contains to a variable extend (30–50%) cDNA that lacks poly(A) and poly(T) sequences. For the study presented here, we did not deplete those cDNAs. Such cDNA can be depleted after a PCR of 2–10 ng of the 10x Genomics PCR product for 5 cycles with 5′-NNNAAGCAGTGGTATCAACGCAGAGTACAT-3′ and 5′ Biotine-AAAAACTACACGACGCTCTTCCGATCT 3′. After 0.55x SPRIselect purification to remove excess biotinylated primers, biotinylated cDNA (in 40 µl EB) is bound to 15 µl 1x SSPE washed Dynabeads™ M-270 Streptavidin beads (Thermo) resuspended in 10 µl 5x SSPE for 15 min at room temperature on a shaker. After two washes with 100 µl 1x SSPE and one wash with 100 µl EB, the beads are suspended in 100 µl 1x PCR mix and amplified for 6–9 cycles with the primers NNNAAGCAGTGGTATCAACGCAGAGTACAT and NNNCTACACGACGCTCTTCCGATCT to generate enough material for Nanopore sequencing library preparation.All PCR amplifications for Nanopore library preparations were done with Kapa Hifi Hotstart polymerase (Roche Sequencing Solutions): initial denaturation, 3 min at 95 °C; cycles: 98 °C for 30 s, 64 °C for 30 s, 72 °C for 5 min; final elongation: 72 °C for 10 min, primer concentration was 1 µM.Mus musculus Genome (mm10) with minimap2 v2.17 in spliced alignment mode (command: “minimap2 -ax splice -uf -MD -sam-hit-only -junc-bed”). The splice junction bed file was generated from the Gencode vM18 GTF using paftools.js, a companion script of minimap2. For reads matching known genes, the gene name was added to the corresponding SAM record (SAM Tag: GE) using the Sicelore (ScNaUmi-seq companion java toolkit) AddGeneNameTag method. Before cellBC and UMI assignment, SAM records were annotated with their Nanopore read sequence (SAM Tags: US) and read qualities (SAM tag: UQ) using the Sicelore AddBamReadSequenceTag method.Nanopore reads were aligned to the Parsing of Illumina data: To retrieve accurate cellBC and UMI information, BAM files with the cellBC and UMI assigned Illumina data generated by the 10x Genomics Cell Ranger software were parsed. For each gene, we extracted the cellBC from the Illumina data and identified the UMIs found for each gene/cellBC combination. We also associated genomic regions (window size 500 nt.) with cellBCs and UMIs to account for reads that match outside of annotated genes. The parsed Illumina data were stored in nested Hash tables as serialized Java objects.Search for poly(A) tail: Our software searches for a poly(T) and a poly(A) sequence  within 100 nucleotides from the 5′ or 3′ end of the read, respectively. Reads without poly (A or T) and reads with a poly(A or T) on both ends are not further analyzed.Search for 3′ adapter sequence: The cellBC and the UMI are located between an adapter sequence (10x Genomics 3′ PCR priming site) and the poly(T) of the reverse transcription primer : To flag reads corresponding to chimeric cDNA generated during library preparation, we searched for internal adapter sequences in proximity of a A- or T-rich sequence and flagged those reads as chimeric reads in the output file. In our dataset we found internal adapters in 3.5% of the reads.Search for cellBCs: Cell barcodes in high accuracy Illumina reads are typically assigned by grouping reads that differ by not more than one position (edit distance: ED = 1). Indels are typically not considered. In consequence just 48 possible permutations of the 16 nt. cellBC reads need to be analyzed and assignment of reads to a barcode is highly reliable. Nanopore reads still have a mean error rate of about 5–10% (substitutions and indels). In consequence higher edit distances need to be examined and indels must be considered. This implies the generation and analysis of 14 barcode sequences for ED = 3. This is clearly not feasible using reasonable sized compute clusters and standard bioinformatics approaches where sequences are typically treated as text. To solve this computational bottleneck, we encoded the barcode sequences using 2 bits per base  which allows encoding of the entire 16 nucleotide cellBC into just one integer. This bitwise encoding allows performing substitutions, insertions, and deletions using highly efficient bitwise operations that require just one CPU cycle. Encoding cellBC and UMIs into integers also tremendously accelerates the search for matching Illumina cellBC or UMIs, since searching for a matching integer is much faster than searching for matching strings. An additional challenge for accurate barcode assignment is that 10x Genomics cellBCs are randomly selected out of a pool of 750,000 barcode sequences. The used barcode sequences are not known in advance. Clustering the Nanopore barcode reads correctly without a priori knowledge of the used barcodes is rather error prone, since two reads that each have e.g., two sequencing errors in the cellBC, can differ in up to four positions when compared to each other. To improve the accuracy of cell barcode assignment, we used the cellBC sequences defined by Illumina sequencing of the same libraries to guide the cellBC identification in Nanopore reads. For each genome aligned Nanopore read, we extracted the sequence  just downstream of the adapter (position corrected for indels in adapter) identified in the previous step. We also extracted the barcode sequences for the preceding and following position to account for eventual terminal indels in the adapter read. We then compared the extracted Nanopore barcode sequences and all possible permutation up to a defined edit distance with: (i) The cellBCs identified by Illumina sequencing for the same gene if the Nanopore read matches a known gene in the Illumina dataset; (ii) Illumina cellBCs sequences for the genomic region (500 nt. window) if the Nanopore read matches an unannotated genomic region or if the gene name was not found in the Illumina data. Since the Nanopore barcodes are only compared with Illumina barcodes found for the same gene or genomic region, the complexity of the search set is reduced. Barcode matches, eventual second-best barcode matches and their quality (edit distance) were recorded in the output BAM file. The maximal ED is dynamically selected (ED limit 1–4) and depends on the number of cellBCs found for the same gene or genomic region in the Illumina data (the complexity of the search set). The software allows to define the maximal false assignment percentage as a parameter. We used computer simulations of collision frequencies to define the complexity of the search set (number of Illumina barcodes) allowed for different edit distances and maximal false assignment percentages. Simulation data are supplied as an XML file and can be easily adapted. Optionally the software also allows the definition of fixed edit distance limits.Search for UMIs: To identify the ten nucleotide UMI, 14 nucleotides  following the end of the cellBC sequence (position corrected for barcode indels) were extracted. For UMI assignment, we used the same strategy as for cellBC assignment. We searched for matching UMIs in Illumina data for the same cellBC and either genomic region or gene identified in the previous step. This means the complexity of the search set corresponds to the copy number of a given gene in just one cell. The maximal allowed edit distance which is dynamically adjusted depending on the complexity of the search set (detailed above).Determination of cellBC and UMI assignment accuracy: To evaluate the accuracy of cellBC and UMI assignment, we first scanned each Nanopore SAM record for a matching Illumina cellBC or UMI. We then repeated the scan where we replaced each cellBC or UMI sequence extracted from the Nanopore read by a random sequence and mutated this random sequence allowing the same number of mutations (edit distance) that was used for the same SAM record in the previous scan and searched for matching cellBC or UMIs in the Illumina dataset. The ratio between the number of reads that were assigned to both a cellBC and UMI after and before replacement of the cellBC or the UMI against a random sequence is the false assignment probability.The cellBC assignment accuracy is particularly high, since after an incorrect cellBC assignment, the UMI is compared with the UMIs associated with this wrong cellBC in the Illumina data. In consequence, most reads with falsely assigned cellBC are subsequently eliminated during UMI assignment. The false barcode assignment rate in cellBC and UMI assigned reads is thus the product of the false cellBC and the false UMI discovery rate. With the default cellBC  and UMI scanning  parameters we used, we obtained an effective cellBC assignment accuracy of 99.8%, which is close to the value expected with those parameters. In a second approach to assess the accuracy of cellBC and UMI assignment we guided the barcode and UMI assignment to the Nanopore reads with Illumina short-read sequencing data from an independent mouse brain single-cell sequencing experiment. The unrelated Illumina dataset was from an 885 cell P18 mouse brain sample .While in the first approach (replacement against random sequences), the maximal edit distance tested for each read was limited to the edit distance for which a match was found (if any) with the non-random sequences, in the second approach, the cellBC and UMI search was allowed to proceed up to the maximal allowed edit distance. E.g. when for a given Nanopore read a cellBC match was found with the correct Illumina dataset at ED = 0, a cellBC match at a higher edit distance in the unrelated Illumina dataset was considered a match.Maximal possible Barcode and UMI assignment efficiency with 10xGenomics data: Since our software scans for matches of the Nanopore cell barcodes with barcodes associated with cells (the relevant barcodes), barcodes associated with empty drops are ignored. The maximal possible barcode discovery rate in Nanopore reads corresponds to the percentage of reads associated with cells in the Illumina dataset: 83.7% and 85.1% for the 190 cell and the 951 cell sample, respectively.The maximal UMI discovery rate for Nanopore reads depends on the Illumina sequencing depth of the same sample and corresponds to the sequencing saturation computed by the 10xGenomics Cell Ranger software after Illumina sequencing. The sequencing saturation is the probability that a matching UMI is found in the Illumina dataset for a given read. For the 190 cell and 951 cell samples, the sequencing saturations were 90.5% and 74.8% respectively. Efficiencies of barcode assignment are given as percentages of cell associated barcodes. UMI assignment efficiencies were corrected for the sequencing saturation.Compatibility of the software: The software is compatible with the 10x Genomics workflow v2 and the recent upgrade (v3) which uses 12nt UMIs. It can also be used for cellBC and UMI assignment of long-read single-cell data generated with other single-cell isolation systems with the following limitations: The cDNA needs to have a 3′ adapter followed by a cellBC, an UMI and a poly(A). CellBC and UMI length, adapter sequences as well as the search stringency for poly(A/T), adapter, cellBC, and UMI can be configured accordingly.Our Java software performs the following analysis steps  just one read, the cDNA sequence of the read was assigned to the UMI; (ii) two reads, the cDNA sequence of the best mapping read  was defined as the consensus sequence; (iii) More than 2 reads, a consensus sequence of all cDNA sequences for the UMI was generated after poa13 multiple alignment and polished with racon14 using all cDNA sequences for the UMI.Potentially chimeric reads  and reads with low-quality genome alignments  were filtered out. SAM records for each cell and gene were grouped by UMI. The cDNA sequence, between TSO end (TE SAM tag) and poly(A) start (PE SAM tag), was extracted for consensus sequence computation using the Sicelore Mus musculus Genome (mm10) with minimap2 v2.17 in spliced alignment mode. SAM records matching known genes were analyzed for matching Gencode vM18 transcript isoforms (same exon makeup). To assign a UMI to a Gencode transcript we required a full match between the UMI and the Gencode transcript exonic structures. We authorized a two-base margin of added or lacking sequences at exon boundaries, to allow for indels at exon junctions and imprecise mapping by minimap2. Following this strategy (Sicelore IsoformMatrix method), we assigned 63.6% of the UMIs to a known Gencode transcript isoform and generated gene-level (median UMIs/cell = 6047) and isoform-level (median UMIs/cell = 3795) count matrices used for the Nanopore/Illumina gene count and UMI count per cell correlations  and the Seurat R package (version 3.1.4). A total of 190 cells and 951 cells were detected with default Cell Ranger cutoffs for the two replicates. Cells with over 95% dropouts were removed. From the 186 and 935 remaining cells , gene expression matrices were cell level scaled to 10.000 and log-normalized. The top 2000 highly variable genes were selected based on the variance-stabilizing transformation method and used for Principal Component Analysis. Due to differences in sequencing depth of both replicates, data were integrated using the Seurat CCA method. The first 11 aligned canonical correlations were used to define the integrated sub space for clustering and t-SNE visualization of the 1121 remaining cells. Clusters in the t-SNE plot were assigned to known cell types using canonical marker genes  combination of known splice junctions, only composed of exon-exon junctions found in Gencode transcripts; (ii) combination of known splice sites, individual donor and acceptor sites are known, but the resulting splice junction is novel; (iii) at least one donor or acceptor site is not found in Gencode transcripts.UMIs of the 1121 cells dataset were used for the identification of novel transcripts isoforms using the Sicelore https://fantom.gsc.riken.jp/5/datafiles/latest/extra/CAGE_peaks/); (iii) a 3′ end within 50 nucleotides of a polyadenylation site .We next filtered novel transcripts isoforms requiring: (i) all exon-exon junctions either described in Gencode or confirmed in an E18 cortex/ midhindbrain Illumina short-read dataset (GEO accession GSE69711); (ii) a 5′ end located within 50 nucleotides of a known transcription start site identified by CAGE  to the median UMIs/cell of the 1121 cell Nanopore transcript isoform-level dataset (3795 UMIs/cell). We then grouped for each replicate cells for each cluster . Heatmaps in Supplementary Fig. To analyze expression correlations between clusters, Illumina gene-level and Nanopore gene- and isoform-level data were downsampled  and inefficiently converted into full-length cDNA in the 10x Genomics workflow. This is likely due to: (i) some RNA degradation within the droplet between cell lysis and reverse transcription. (ii) internal reverse transcription priming at A-rich sites within the cDNA leading to cDNAs that cover only part of the transcripts. In consequence, we noticed 3′ bias and fragmented coverage for certain long transcripts such as Gria2 where a total of 456 cDNA molecules (UMIs) had the R/G-editing-and 233 had both the R/G and the Q/R-editing-site. Further optimization of the 10x Genomics workflow should allow more efficient full-length capture of long mRNAs.9593 reads (2105 UMIs) corresponding to Gria2  were extracted from the 951 and the 190 cell dataset. A consensus sequence for each molecule was computed and re-mapped to the mm10 genome for SNP calling using the Sicelore Further information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting SummaryDescription of Additional Supplementary FilesPeer Review File


Saccharomyces cerevisiae. Proximity in low-dimensional UMAP space identifies groups of genes that correspond to protein complexes and pathways, and finds novel protein interactions, even within well-characterized complexes. This approach is more sensitive than previous methods and should be broadly useful as additional transcriptome datasets become available for other organisms.Dimensionality reduction is often used to visualize complex expression profiling data. Here, we use the Uniform Manifold Approximation and Projection (UMAP) method on published transcript profiles of 1484 single gene deletions of  Dimensionality reduction is often used to visualize expression profiling data in order to find relationships among cells. Here, the authors use Uniform Manifold Approximation and Projection (UMAP) on published expression data of gene deletions of S. cerevisiae to find novel protein interactions. For the yeast Saccharomyces cerevisiae, the expression profiles of a large number of individual yeast deletion mutants have been established and used to infer protein complexes and networks4. Maximizing the utility of expression profiling approaches for inference of physical and genetic interactions requires ever larger such datasets. However, standard techniques, such as pairwise correlation, do not fully capture the variation available to link gene function as more dimensions are added from larger scale experiments. Therefore, techniques that reduce dimensionality of the data while maintaining relationships between genes are imperative for the inference of physical and genetic interactions in very large gene expression datasets.A central goal of biological studies is the identification and characterization of proteins that act in a common cellular pathway. Efforts toward this goal have been greatly aided by large-scale perturbation analyses coupled with whole-transcriptome profiling, in which each gene’s transcriptional response to a perturbation is measured. If a sufficient database of expression profiles exists, then a pathway affected by an uncharacterized perturbation such as a gene mutation, drug treatment or growth condition—can be described by matching the resultant profile to a known profile5. A more recent method, Uniform Manifold Approximation and Projection (UMAP), estimates a topology of the high-dimensional data and uses this information to construct a low-dimensional representation that preserves relationships present in the data6. UMAP has been particularly useful to precisely define cell types in mixed populations based on data from single-cell RNA-seq experiments13; it also performs well on other gold-standard datasets14. Because UMAP is better able to preserve elements of the data structure from high-dimensional space than similar outputs from t-SNE, it captures local relationships within groups of transcriptomes in addition to global relationships between distinct groups14. This feature is especially useful in the inference of gene relationships, which can be due to physical interaction, overlapping gene function, or coordinated contributions to a larger cellular process. Here, we show that the use of dimensionality reduction by UMAP on bulk expression profiling data of 1484 single-gene mutants of S. cerevisiae links gene function in clusters at increasingly finer scales, corresponding to broad cellular activities, pathways, protein complexes and individual protein-protein interactions.Dimensionality reduction methods capture variability in a limited number of random variables to facilitate 2- or 3D-visualization of datasets with tens to thousands of dimensions. This approach is recognizable in the commonly used method of principal component analysis (PCA), which uses linear combinations of variables to generate orthogonal axes that efficiently capture the variation present in the data with fewer variables. Another approach, t-Distributed Stochastic Neighbor Embedding (t-SNE), carries out dimensionality reduction by analyzing similarity of points using a Gaussian distance in high-dimensional space and projecting these data into a low-dimensional space9. While many single-cell transcriptomic studies use expression values from genes with the highest dispersion across individual cells, we took advantage of the completeness of bulk microarray data generated by Kemmeren et al.3 and used expression values for all 6170 genes measured in each of the 1484 single-gene deletion strains to make a UMAP projection for subsequent clustering. This approach resolved 50 main clusters, with the number of deletion backgrounds assigned to each cluster ranging from 4 to 298 (median of 11). Clusters with >25 strains were subsequently sub-clustered using similar parameters to define groups. The final dataset contains 171 clusters with a median of 8 strains per cluster.We assigned groups, or clusters, to deletion mutants with similar transcriptional responses using the Louvain community detection algorithm in low-dimensional UMAP spaceA total of 194 characterized yeast complexes have at least two of their corresponding genes in the dataset of single deletions. For 40% of these complexes (78/194), we could assign two or more genes to the same cluster  containing 3, 6, and 4 members of mediator, respectively Fig. . ClusterURM1 pathway for 2-thiolation and the Elongator complex for side chain formation at U34 of tRNA15. The clustering revealed two additional members that are likely to link metabolism and cell cycle to this process. One of these, Met18, has a human ortholog (MMS19) that functions in maturation of Fe-S cluster-containing proteins; the conserved yeast and human Elongator component Elp3 is one of these Fe-S proteins16. The other new member, the PP2A phosphatase Sit4, is implicated in dephosphorylation of Elongator; its absence leads to tRNA modification defects17.UMAP clustering identified the components of the pathway for tRNA wobble uridine modification Fig. , which r2; (2) gene interactions from stringDB18, which are derived from a probabilistic metric based on multiple evidence channels including yeast two-hybrid, pathway annotations, and co-expression; and (3) interactions from CellMAP19, which are derived from an experimental screen for synthetic genetic interactions. The UMAP distance metric captured protein complexes more sensitively and with more precision than previous pairwise correlation-based metrics  protein interactions determined by co-immunoprecipitation followed by mass spectrometryPerforming clustering in UMAP space ought to produce clusters containing more true interactions than distance in other spaces. To test whether similar results were obtained without UMAP dimensionality reduction, we clustered the data in PCA space. Clustering in PCA space identified 8/50 clusters with perfect overlap to UMAP clusters, and 34/50 that overlap by at least 50%  the Pearson correlation of their deletion transcriptomes; and (2) the distance of those two genes in the UMAP space generated by using all deletion transcriptomes. Among these interacting pairs, UMAP distance and pairwise correlation are negatively correlated Fig. . HoweverR = 0.018) or Gene Ontology relationships (R = 0.035)19. When paralogs show a negative genetic interaction—that is, deletion of both genes leads to lower fitness than expected—it is assumed that the two genes retain redundant functions. However, 11 paralog pairs whose negative genetic interactions suggested redundant function showed distinct downstream effects on gene expression when each gene was deleted 9 to project single-gene deletion strains into two dimensions and subsequently used Louvain clustering25 on strains in 2D UMAP space using default parameters . Prior to dimensionality reduction, expression values from all 6170 yeast genes were given as input to Principal Component Analysis (PCA) using the Monocle 3 wrapper function “preprocess_cds”. The top 100 principal components were then used as input to UMAP for generating 2D projections of the data. For subclustering, main clusters 1–10 were each individually processed using top 25 principal components in the subset data as input to UMAP dimensionality reduction and Louvain clustering (resolution = 1e-4).To project single-gene deletion strains into two dimensions we performed dimensionality reduction with the UMAP algorithmAlternative dimensionality reduction with tSNE was performed using the Monocle 3 function reduceDimension with default parameters (reduction_method = tSNE). Dimensionality reduction using random projection, based on the Johnson-Lindenstrauss lemma, was performed using the RandPro (v0.2.0) R package.26.Gene expression values for single-gene deletions within a cluster were compared to the background set of all deletions. Differentially expressed genes for each cluster were calculated using the differentialGeneTest function in Monocle 3. Because the expression datasets were microarray-derived rather than count-based RNA-seq data, the “gaussianff” expression family was used; significance values were corrected for genomic inflation factors using lamba gc2. The consensus set was transformed into a pairwise Boolean interaction matrix based on whether or not each pair had been observed together in the known complex set. Using the subset of pairs that were found in the set of 1484 single-gene deletion transcriptome datasets, for each gene pair, we calculated Euclidean distance in UMAP space. We then used these distances, along with labels for true and false interacting gene pairs derived from gold standard interaction datasets, to generate receiver operating characteristic (ROC) and precision/recall curves with the PRROC package in R27.To test the ability of UMAP distance, and other distance metrics, to capture known interactions, we used a curated consensus set of protein complexes derived from two large, high-throughput mass spectrometry datasets and GO interactionsFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Regime shifts in ecosystem structure and processes are typically studied from a temporal perspective. Yet, theory predicts that in large ecosystems with environmental gradients, shifts should start locally and gradually spread through space. Here we empirically document a spatially propagating shift in the trophic structure of a large aquatic ecosystem, from dominance of large predatory fish  to the small prey fish, the three-spined stickleback. Fish surveys in 486 shallow bays along the 1200 km western Baltic Sea coast during 1979–2017 show that the shift started in wave-exposed archipelago areas near the open sea, but gradually spread towards the wave-sheltered mainland coast. Ecosystem surveys in 32 bays in 2014 show that stickleback predation on juvenile predators  generates a feedback mechanism that appears to reinforce the shift. In summary, managers must account for spatial heterogeneity and dispersal to better predict, detect and confront regime shifts within large ecosystems. Eklöf et al. report a spatially propagating shift in the trophic structure along the western Baltic Sea coast. The authors use fish surveys from 1979–2017 to show a shift from dominance of large predatory fish to the small prey fish, the three-spined stickleback, starting in wave-exposed areas and gradually moving to the wave-sheltered coast. In parallel, theoretically driven mathematical models triggered the intriguing idea that ecosystems can display multiple stable states under similar conditions4. Following intense debates regarding the evidence for such “multi-stability” in the real world5, the reconciliation of observation and theory under the umbrella concept “regime shifts”—which we here define as abrupt and long-term changes in ecosystem structure and functions, including shifts between stable states8—facilitated an exponential growth9 in efforts to identify, predict and reverse ecosystem shifts across the world’s biomes10. Today we know that regime shifts are typically caused by external “shocks” or gradually changing environmental conditions that exceed critical thresholds (a.k.a. “phase shifts”), but that some also involve critical transitions10 where novel feedbacks propel the system from one self-reinforcing and persistent regime (or stable state) to another10. All regime shifts are difficult to manage, but critical transitions pose particular challenges because of inherent difficulties in both predicting and reversing them10.Over the last half century, ocean and coastal ecosystems have been increasingly observed to sometimes shift unexpectedly to alternative and seemingly persistent sets of dominating species and processes (regimes) in response to environmental and biotic changes14; a paucity stemming from the fact that many model systems  are relatively small, homogenous and have hard physical boundaries. However, recent advances suggest that large, heterogeneous ecosystems with permeable boundaries  can instead display spatial regimes, i.e. spatially explicit sets of similar structures and functions maintained by self-reinforcing feedback mechanisms within their boundaries16. Theory predicts that in such systems, gradual environmental change can trigger the initiation of local (patch-level) sudden shifts in ecosystem structure, that are asynchronous because of the spatial heterogeneity12. Consequently, the whole-system response becomes gradual  rather than “catastrophic” (threshold-like)17. If organisms also disperse between the patches, the shift should start in areas where systems are closest to environmental thresholds, but then gradually spread as a traveling wave, front or falling dominos17—similar to spread of epidemic disease, financial crises and revolts in societies10. However, evidence for such spatial or “gradual”13 regime shifts4 in nature is limited to small-scale dynamics of desert vegetation18 and decadal changes in the distribution of grassland bird assemblages19.Despite well-developed theory and many empirical examples, we are still far from understanding and managing regime shifts. One particular challenge is that most studies ignore the role of spatial variability22. By releasing smaller consumers from top-down control23, predator decline can cause long-lasting shifts to prey-dominated regimes with profound impacts on ecosystem structure and function25. Such regime shifts are often caused by predator overharvest and/or gradual changes in environmental conditions22, but can also be accelerated by novel feedback mechanisms, that may stabilize the alternative regime and prevent natural recovery26. One such feedback is predator–prey role reversal27, where predator decline benefits small prey organisms that, in turn, prevent predator recovery by feeding on their early life stages28. Such role reversal has mainly been studied from a temporal perspective, but can in theory trigger traveling waves of prey dominance29.In oceans and lakes across the world, declines of large predatory fish disrupt ecosystem functioning, economies, and human livelihoods30) that typically play out over time, but sometimes also over space. One of the most conspicuous examples is consumer fronts; hyperdense aggregations of mobile consumers  along resource edges31. The universal mechanism is that increasing consumer abundance leads to local overconsumption of resources, which triggers resource-dependent movement and, ultimately, the formation of fronts that propagate like traveling waves32. The fronts are typically transient or sometimes cyclic32 and dissipate because food resources become limiting, and/or because consumers are dominated by large cohorts that naturally die or disperse31. However, theory30 and observations33 suggest that if the consumers are instead generalists that can switch to a more common prey, the fronts may lead to persistent regime shifts.Simple predator–prey interactions can generate complex system-level phenomena —rapidly increased nearly 50-fold in offshore areas34. Various evidence suggests that reduced predation pressure from declining stocks of large predatory fish increased stickleback survival37, while eutrophication38 and rapid ocean warming39 increased their population growth40. Because adult stickleback migrate from the open sea to the coast in spring to breed, they effectively link coastal and offshore processes37. Along the coast, two large predatory fish species—Eurasian perch  and Northern pike —can locally control stickleback abundances36. However, predator abundances have declined along parts of the coast, potentially due to fisheries42, increased seal and cormorant predation42, and degradation of recruitment habitats43. Consequently, stickleback today dominate large coastal areas and there generate a trophic cascade that increases algal blooms, degrades habitat-forming benthic vegetation and exacerbates effects of eutrophication44. Recently, stickleback have also been shown to feed on perch and pike larvae, potentially suppressing their recruitment45. This indicates that predator–prey reversal may have reinforced a shift to a stickleback-dominated regime37.Here we describe a spatial shift from a predator- to prey-dominated marine ecosystem regime that has gradually spread through the 1200 km western Baltic Sea coast (northern Europe) over the last four decades. Starting in the early 1990s, abundances of a common mesopredatory fish—three-spined stickleback  to small (stickleback) predatory fish dominance, gradually propagating from areas near the open sea towards the mainland coast Fig. . The reaUsing a unique dataset of fish surveys in nearly 500 shallow bays over 39 years we confirm the existence of a spatial shift to stickleback dominance, gradually spreading from outer archipelago areas toward the mainland coast. The longest time series from a single area (Forsmark) shows that local shifts may be abrupt with clear temporal breakpoints, even though the large-scale spatial shift is gradual . Finally, a detailed ecosystem survey in 32 bays shows that stickleback suppression of juvenile perch and pike  forms a strong feedback mechanism that appears to reinforce the shift. These findings emphasize that detecting and confronting regime shifts in spatially extended ecosystems will require addressing underlying drivers while accounting for spatial heterogeneity and organism dispersal.46. Since 90% of the observations belong to one of the two modes  in 477 shallow bays during 39 years (1979–2017), spread across 1200 km of the western Baltic Sea archipelago coast (55–65°N). Most (75%) of the bays were sampled once but some up to 34 years, resulting in 846 unique bay-year combinations. For each bay-year we then calculated the relative predator dominance (perch + pike abundance/perch + pike + stickleback abundance); a ratio from 1 to 0 where 1 = 100% perch and pike dominance and 0 = 100% stickleback dominance. As expected from theory Fig. , the ratdes Fig. , this moThe best-fitting model confirms a gradual shift to stickleback dominance that propagated from the outer to the inner archipelago, evident by a strong interaction between time and distance to open sea, as well as main effects of distance, wave exposure and latitude Fig. . We find1 we also tested for a temporal shift from perch to stickleback dominance at the local scale, as evident by change-point(s)47 in the longest time-series from an individual bay in our dataset; a 34-year sampling program outside Forsmark . There was a change-point in year 2004, separating long-term perch dominance from stickleback dominance  and an exponential stickleback increase starting in the early 2000s . This sequence of events supports the hypotheses that mesopredator release contributes to the rise of the stickleback36. Moreover, the 2004 breakpoint separating perch and stickleback dominance supports that even though the regional change in dominance is gradual . Pooled perch and pike juvenile abundance declined exponentially over time across the entire coast, after accounting for a positive influence of distance to the open sea and a negative influence of wave exposure , and included several direct and indirect relationships that together demonstrate the pivotal role of predator–prey reversal for piscivore (perch and pike) recruitment ; a temporally lagged relationship mediated by a positive, direct influence of adult stickleback abundance in spring on stickleback recruitment. High adult piscivore abundance in spring also positively influenced piscivore juvenile abundance in summer. However, this relationship was indirect and mediated by a strong negative influence of adult stickleback abundance in spring on piscivore recruitment (juvenile abundance in summer). This predator–prey reversal path is supported by experiments and field surveys45 and was needed for the models to fit the data well  and food (zooplankton) availability in 32 shallow bays along a 360 km stretch of the central coast, selected to form gradients in distance to the open sea and wave exposure  and warming increased stickleback population growth52. In turn, predation by the hyper-abundant stickleback on benthic grazers and zooplankton generates system-wide trophic cascades that benefit fast-growing, filamentous algae53 at the expense of habitat-forming benthic vegetation36 and reduces resilience to eutrophication54. Our ecosystem field survey, supported by past experiments45, also shows that stickleback suppresses perch and pike recruitment through predator–prey reversal  reached by the stickleback front.To our knowledge this study is the first to describe a regime shift that spatially propagates through a marine ecosystem. Combined with previous studies on the stickleback increase, our findings highlight how environmental and biotic changes together facilitated the rise and spread of stickleback dominance: reduced predation pressure due to declining stocks of large predatory fish along the coast and in the open sea increased stickleback survival31; a fact most likely explained by stickleback (i) spreading over an increasingly large area and (ii) being extreme generalists not limited by certain prey types56, effectively reducing intraspecific competition associated with their dramatic population increase57. On the other hand, several of the likely drivers of the shift  have gradually increased over time along with stickleback abundance  scales to be effective.Whether this spatial regime shift constitutes a “critical transition” to a stable stickleback state is still an open question. On the one hand, our results give clear hints of a critical transition; (i) bimodality over time and space, even before the major stickleback increase Fig. , (ii) suift Fig. , indicat17. Therefore, the front should in theory continue to expand towards the mainland coast, even though perch and pike juveniles may still escape predation in the most wave-sheltered mainland bays and freshwater tributaries45. Consequently, halting or even reversing a spatially gradual regime shift may require unprecedented interventions that reverse underlying drivers at sufficiently large spatial and temporal scales17. This could in theory include (i) strengthening stocks of piscivorous fish that feed on stickleback  through fisheries regulations along the coast and in the open sea35, culling of fish-eating top predators like seals and cormorants42, and restoring and protecting spawning and nursery areas of predatory fish, (ii) reducing stickleback densities through fisheries34, and (iii) strengthening efforts to reduce eutrophication and climate change. Whether such actions could halt the stickleback front and even facilitate a reverse expansion of a perch and pike-dominated regime towards the open sea, increasing the many ecological, economic, and cultural values that these large predatory fish support, remains to be explored.We do not yet know whether the spatial shift to stickleback dominance can be halted, but theoretical models provide interesting predictions. First, spatial or “gradual” regime shifts themselves indicate that the potential for system-wide hysteresis is lowPerca fluviatilis, northern pike Esox lucius) and three-spined stickleback (Gasterosteus aculeatus) changed over time and space, we collated juvenile fish abundance data from 13073 samplings conducted during 39 years (1979–2017) in 486 bays along a 1200 km stretch of the Swedish Baltic Sea coast. We used juvenile fish surveys because they (i) include stickleback, (ii) were conducted along the entire coast, including the outer archipelago, and (iii) capture patterns of recruitment failure; a proposed driver of local perch and pike stock decline45. The samplings were conducted by various monitoring programs and research projects to quantify fish recruitment. Much of the data was extracted from the Swedish national database for coastal fish (http://www.slu.se/kul). Other fish species also occurred in the data, but we—like others45—focused on the most common and strongly interacting species.To assess how the dominance of large predatory fish  gradients in distance to open sea and wave exposure, (ii) the entire Swedish east coast, and (iii) the longest time period possible , 2–20 cm fish are sampled within approximately a 5 m radius (ca. 80 m2). The fish are then collected using swing nets and snorkeling, identified and counted. All sampling was conducted by certified personnel and with required ethical permits.The surveys were conducted in shallow coastal bays at 1.7 ± 0.6 depth . Juvenile fish were sampled using low-impact pressure waves; a standard method in the area59. For surveys only reporting floating fish, we also calculated the expected total number of fish using the sunken:floating fish ratio from other surveys (N = 47–152 per species).The type and amount of explosives varied between surveys (1–25 g), and some surveys noted only floating (not sunken) fish. To account for these differences, we recalculated the total number of individual fish per species to the current detonation standard using experimentally derived conversion factorsrelative predator dominance . We primarily used relative abundance because it captures the community state well62 and reduces the “noise” in absolute abundance caused by the notoriously high year-to-year variability in fish recruitment63. Perch constituted 96% of the pooled abundance of perch and pike, but we included pike as well because they were occasionally more common than perch.Since the method samples highly mobile organisms within a small surface area and very short time span, it is prone to high variability and false absences. We therefore averaged the species abundance of perch, pike and stickleback per bay and year (based on 5–177 samplings [detonations] per bay-year: mean ± SD = 15.6 ± 12.2). Most (75%) of the 477 included bays were sampled once, but 25% during multiple 2–34) years, generating 833 unique bay-year combinations. Since fish assemblages within a single bay can shift from perch and pike to stickleback dominance , water surface temperature , salinity  and water visibility . Finally, we for each bay used GIS to calculate the distance to open sea (m shortest water distance from the baseline) and the relative wave exposure (m2 s−1), based on a surface wave model (SWM) utilizing fetch and wind data averaged across 16 compass directions. The model mimics diffraction using empirically derived algorithms .We also included data on four covariates known to influence juvenile fish abundanceTo test for a more classic temporal regime shift at the local  scale, we used the longest time-series from a single area; a 34-year data set from an annual monitoring program  conducted outside Forsmark . The data was collected in an area used as a reference for estimations of effects of the release of heated cooling water from the Forsmark nuclear power plant. Only perch and stickleback (no pike) occurred.49. In 2014, we surveyed 32 shallow bays (<3.5 m depth) situated >10 km apart  along a 360 km stretch of the central Swedish Baltic Sea coast  and density of zooplankton . Zooplankton were sampled using a 25 cm diameter Epstein net (80um mesh), slowly pulled three times vertically from 0.7 m above the seabed to the water surface. Zooplankton were fixated in 5% formalin and then counted in the lab. In late summer (August) we at 6–8 stations per bay estimated fish recruitment  and % cover of habitat-forming benthic vegetation. The fish sampling was evaluated and approved by the ethical board on animal experiments of the County court of Uppsala, Sweden, permit C 139/13.To assess the relative importance of predation and predator–prey reversal for juvenile fish recruitment, we combined an ecosystem field survey with causal path analysis; a powerful approach to tease apart the role of direct and indirect interactions in ecosystems bays <3. m depth Spatial regime shifts over time and space: we hypothesized that a shift from perch and pike to stickleback dominance started in wave-exposed bays in the outer archipelago, but then propagated towards the inner archipelago over time  at the whole-system scale13. Moreover, while multiple drivers could in theory influence whether bay assemblages are perch and pike- or stickleback-dominated, change-point detection methods cannot handle multiple predictors and interactions between them. Finally, our response variable (relative predator dominance) is bounded between 0 and 1 and was bimodal, based on Hartigan’s dip test of unimodality65 with 10,000 permutations , distance to open sea and their interaction on relatively predator dominance, while controlling for the influence of wave exposure (log-transformed) and latitude. After assessing assumptions of homoscedasticity by plotting deviance residuals vs. observed values for each predictor , (ii) refitted the best binomial model using this new dataset, (iii) extracted the estimates, standard deviations and standard errors for each parameter, (iv) repeated the whole procedure 500 times, and iv) summarized the average results (Supplementary Table N = 477 vs. 833 in the full dataset), as well as the influence of randomly including more perch- and pike- or stickleback-dominated years from the 25% (131) bays sampled >1 year.Since 75% of the bays were sampled only once we could not include “bay” as a random factor. To assess the robustness of our results given the repeated sampling in 25% of the bays, we (i) generated a smaller dataset including one randomly chosen observation (year) per bay N = 77, (ii) maximum spatial extent of stickleback dominance along the archipelago gradient increased over time, we first selected all stickleback-dominated bay-years . For each sampling year we then extracted the maximum distance from open sea, separating wave-sheltered vs. -exposed bays (using a cutoff of log10(m2 s−1) >4). Since no bays were sampled >20 km from the open sea prior to 1995, we only used data from 1995–2017. Finally, we used a general linear multiple regression model to test how sampling year, wave exposure (two levels) and their interaction explained the maximum distance.To test whether the R2 = 0.11), and the bimodality clearly remained  predators (perch and pike pooled) and (ii) stickleback, using general linear models. We identified the most parsimonious models as outlined above.Temporal regime shift at Forsmark: using the 34-year Forsmark time series, we first tested for temporal breakpoints in logit-transformed relative predator dominance data using change-point detection (strucchange) for linear models47. This method estimates the optimal number and (if identified) position of breakpoints using the Bayesian Information Criterion (BIC). Second, we explored what temporal change(s) in perch and stickleback abundances that preceded the shift. Because of highly non-linear patterns we modeled the temporal changes using generalized additive models (GAM) as implemented in the mgvc package68.Importance of predator–prey reversal for fish recruitment: to assess the relative importance of predator–prey reversal for perch, pike and stickleback recruitment, we used statistical model selection based on path analyses; a form of structural equation modeling that can be used to tease apart direct vs. indirect (mediated) relationships between multiple (>2) variables, and thereby assess the relative importance of direct vs. indirect relationships in systems69. Initial data exploration using multiple regression showed that one bay was a clear outlier due to 0 juvenile perch and pike, generating (i) too high leverage , (ii) heteroscedasticity and (iii) non-normally distributed errors. Since we suspected that juveniles had already migrated out of this bay, the bay was excluded (resulting in N = 31). Removing this statistical outlier resulted in that the model fulfilled test assumptions and the overall fit more than doubled (adjusted R2 increased from 0.17 to 0.37).49. Due to the relatively low sample size we restricted the number of paths to 7. The two simplest models assumed that perch+pike and stickleback juvenile abundance in summer (i.e. recruitment) was influenced by adult abundance in spring (stock recruitment) and cumulative cover of rooted vegetation, while adult abundance was explained by spring cumulative cover of all vegetation species46, and distance to open sea or wave exposure46. The more complex models included combinations of known predator–prey interactions: perch and pike controlling adult stickleback in spring through predation36, stickleback feeding on juvenile perch and pike45, and stickleback competing with juvenile perch for zooplankton prey37. We then analyzed each model using piecewise path analysis as implemented in the piecewiseSEM package48. First, we tested the goodness of fit of each model to the data using Shipley’s test of directional separation (D-sep)70. If missing paths were identified, they were included in a new model. For the models that fitted the data (p > 0.05) we used the Akaike’s Information Criterion corrected for small samples (AICc)  to compare relative model fit. A summary of all candidate models and details of the best-fitting model are presented in Supplementary Tables 2) in adult stickleback abundance, pooled juvenile perch and pike, and juvenile stickleback abundance, that was explained by the paths. Finally, we tested whether the relative predator dominance of adult and juvenile fish in this smaller dataset (N = 31) was also uni- or bimodal, using Hartigan’s dip test .Based on ecological knowledge of the study system, we expressed 14 multivariate hypotheses of the direct and indirect drivers of perch and stickleback recruitment, as graphical network models of interacting pathsFurther information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Description of Additional Supplementary FilesReporting SummaryPeer Review File


To analyze the low to medium distractor efficiency items in a multiple-choice question (MCQ) paper for item writing flaws.nd year MBBS class, was analyzed to determine the non-functional distractors (NFDs) and distractor efficiency (DE) of items, in a total of 181 MCQs. DE was categorized as low (3-4 NFDs), medium (1-2 NFDs) and high (0 NFD). Subsequently, qualitative document analysis of the MCQ paper whose item analysis report was assessed was conducted to investigate the item flaws in the low to medium DE items. The flaws identified were coded and grouped as, within option flaws, alignment flaws between options and stem/ lead-in and other flaws.This qualitative study was conducted at Islamic International Medical College Rawalpindi, in October 2019. Archived item- analysis report from a midyear medium stakes MCQ paper of 2Distractor efficiency was high in 69 items (38%), moderate in 75 items (42%) and low in 37 items (20%). The item-writing flaws identified in low to moderate DE items within distractors included, non-homogenous length (1.8%), non-homogenous content (8%) and repeat in distractor (1.7%). Alignment flaws between distractors and stem/ lead-in identified were linguistic cues (10%), logic cues (12.5%) and irrelevant distractors (16%). Flaws unrelated to distractors were low cognitive level items (40%) and unnecessarily complicated stems (11.6%).Analyzing the low to medium DE items for item writing flaws, provides valuable information about item writing errors which negatively impact the distractor efficiency. Post hoc item analysis is a commonly used tool to assess the quality of Multiple-choice questions (MCQ) based examinations in undergraduate medical education. It provides useful information about the reliability and validity of test items. The parameters commonly assessed in item analysis are; the discrimination index (DI), difficulty index and distractor efficiency (DE). Functional or efficient distractors are those, chosen by more than 5% of examinees whereas the distractors chosen by less than 5% examinees are known as non-functional distractors.In an MCQ item, the context or background is referred to as the ‘stem’, followed by the question known as the ‘lead-in’ and a number of option choices. High-quality MCQs require a well written unambiguous stem, clear lead-in and rational choice of options. In a one best type of MCQ, one of the options is the correct response known as the ‘key’ while others are described as ‘distractors’.6In depth study of items showing low distractor efficiency can help test-developers and instructors understand test-wiseness of students in relation to item flaws. Distractor efficiency also has an indirect effect on the item difficulty as well as its discriminatory ability.8Not much work has been done on the qualitative aspect of individual items with low distractor efficiency. The objective of this study was to analyze the item writing flaws in low to medium distractor efficiency items in a multiple-choice question (MCQ) paper in order to gain insight into the structural flaws in items which negatively impact the distractor efficiency, overall exam quality and student performance. This study will help the test developers be aware of item flaws and address them in a more logical and systematic manner.This qualitative study was conducted at Islamic International Medical College, Riphah International University in October 2019. Ethical approval was taken from Institutional review committee of Islamic International Medical college, Ref #Riphah/IIMC/IRC/20/005.nd year MBBS class was analyzed to assess the distractor efficiency of items. There were 181 single best response type MCQs having a reliability coefficient of 0.88. One hundred and six (106) items had five option choices and 75 items had four option choices.Archived item- analysis data report from a midyear medium stakes MCQ paper of 2Nonfunctional distractors (NFD) were identified as the distractors chosen by less than 5% examinees. Distractor efficiency (DE) was defined on the basis of the number of NFDs in an item and ranged from 0-100%. Distractor efficiency of items was graded as low (having 3-4 NFDs), medium (having 1-2 NFDs) and high (having 0 NFD).Subsequently, qualitative document analysis of the MCQ paper whose item analysis report was assessed, was carried out independently by two reviewers, who evaluated each low to moderate DE item, for item writing flaws. The reviewers were experienced in MCQ test item development and were also trained in MCQ writing. Item flaws were investigated with reference to item writing guidelines proposed by Haladyna et al. and followed in literature.Flaws within optionsAlignment flaws between options and stem/ lead-inOther flaws, unrelated to options or their alignment with stem/ lead-inA total of 649 distractors were identified in 181 MCQs out of which 205 were nonfunctional distractors (31.6%). Out of these 181 MCQ items, 112 items were low to moderate distractor efficiency items. Distractor efficiency of items was determined as given in Out of the 112 low to moderator distractor efficient items, 62 items  had one or more item writing flaws in the option choices or their alignment with the stem and lead-in. Of the remaining 50 items; flaws unrelated to the options or their alignment with stem/ lead-in were found in 27 items, these were low cognition level items or complicated stems; whereas no item writing flaw was identified in 23 items. The item flaws identified are given in The most frequently identified flaws were ‘low cognitive level items’ (40%), ‘irrelevant/ limited possible options or distractors’ (16%), followed by ‘logic cues’ and (12.5%) and, unnecessarily complicated stems (11.6%).We aimed to identify different types of qualitative item flaws in MCQ items having low to moderate distractor efficiency. Distractor efficiency was low to moderate in 62% items in our study. Various studies show comparable results with low to moderate distractor efficient items in the range of 31% to 75% in various local studies,14The frequency of flawed items in the 112 low to moderate distractor efficient items in our study was 89/112, (79%) which was 49% of the total MCQ items. In a study by Pais et al., 55.8% items had at least one item writing flaw. This high frequency was similar to other studies, where around half of the items contained item writing flaws.17Lower-order cognition items were a frequently identified flaw (40%). Ideally constructed MCQs should be written at a level of difficulty appropriate to level of the students and the focus of assessment should not be students’ knowledge of inconsequential or trivial facts.20The predominant item flaw identified following the ‘low cognition items’ was ‘limited number of possible logical or plausible distractors’ 16%) which was in the category Alignment flaws between the distractors and the stem or lead-in’. In a study by Salih et al., implausible distractors were cited as a frequent flaw (25%).% which w9Another frequently identified flaw in our study was ‘logic cues’ due to inadvertent use of specific words or themes in distractors and stem or lead-in directing towards the correct answer 12.5%). In some other studies these were less frequent (4-6%).%. In som11Ambiguous/ confusing stem or lead-in was found to be 11.6% in our study. In various studies the frequency of this flaw ranged from 19-50%.The least frequent flaws in our study were in the category, ‘within options flaws’ 11.5%). This was in accordance with the study by Salih et al..5%. This11No item writing flaw was identified in 20% items having low to moderate distractor efficiency. One of the possible reasons may be that the item psychometric statistics from only one administration of the test was studied, and the options identified as non-functioning distractors in this exam may have been subject to sampling bias and may perform quite differently in other samples.6A limitation of the study is, that a single paper was analyzed for item flaws based on its item analysis index of low DE.Analyzing the low to medium DE items for item writing flaws, provides valuable information about item writing errors which negatively impact the distractor efficiency. Correcting the errors can improve distractor efficiency and overall exam quality.RAK: Conceived, designed reviewed and did final approval.MS and SI: Did data collection, manuscript writing, editing and review.All authors are responsible and accountable for the accuracy and integrity of the work.


We will discuss recent advances in the development of nanoparticle vaccines presenting HIV-1 envelope trimer vaccines and the immunological mechanisms by which they act.in vitro. Immmunogenicity studies with these nanoparticles delineate the advantages and expose the limitations of the different nanoparticle platforms.The multivalent presentation of Env trimers on nanoparticles is a promising strategy to increase Env immunogenicity. Recent studies have shed light on how Env nanoparticles increase lymph node trafficking and germinal center formation by using the lectin-mediated complement pathway and enhancing the interaction with naïve B cells. Meanwhile, research on different nanoparticle platforms has resulted in improved designs, such as liposomes with improved stability, and the emergence of novel platforms such as protein nanoparticles that self-assemble It is becoming increasingly clear that HIV-1 vaccine research might benefit greatly from using nanoparticles presenting Env trimers, particularly during the priming stage of immunization. Among the different nanoparticles that are being pursued, in vitro-assembling nanoparticles allow for greater control of Env quality making them a promising nanoparticle platform. With around 2 million new cases of HIV worldwide the need for a vaccine that can prevent infection remains as high as ever. To deal with the large sequence diversity of HIV-1 it is widely accepted that a successful vaccine should induce broadly neutralizing antibodies (bNAbs) . These sThe success of the commercially available HPV and HBV vaccines and the promising preclinical data for influenza, EBV and malaria nanoparticle vaccines, reinforce the paradigm that multivalent presentation of an immunogen can enhance its immunogenicity –14. In let al. provided new insights into the immunological mechanisms behind the improved trafficking to lymph nodes of nanoparticles carrying highly glycosylated antigens such as Env [▪▪]. Nanoparticles presenting Env trimers where present in lymph node follicles, germinal centers, and on fDCs in much higher quantities than their soluble counterparts. This was shown to be largely dependent on complement, as colocalization of particles on fDCs was significantly reduced in mice lacking complement component C3 or its receptors Cr1/2. They further showed that mannose-binding lectin bound significantly better to nanoparticles than soluble immunogens and that the mannose-binding lectin pathway mediated the observed fDC targeting [▪▪]. Thus, as a result of increased avidity by multivalent presentation of Env, nanoparticles more efficiently initiate the mannose-binding lectin – pathway resulting in higher deposition of complement. This subsequently leads to more efficient trafficking to lymph node follicles and presentation on fDCs.To mount an antibody response, an immunogen has to find its way into the draining lymph nodes where it will interact with B cells and follicular dendritic cells (fDCs). In the case of direct lymphatic drainage, antigens will drain from the administration site by interstitial flow towards a lymphatic vessel. As particulate antigens have lower diffusion rates they will be delivered more effectively than monomeric antigens . Recentlh as Env ▪▪. Nanopargeting ▪▪. Thus,in vitro  [et al.[▪▪] tested the effects of immunogen affinity and avidity on germinal center formation in mice that had a range of precursor frequencies of a specific germline B cell. At physiological affinities (0.5 μM KD) and frequencies (one in 1 × 106 cells), nanoparticles were able to expand specific germline B cells whereas with the soluble counterpart these B cells were undetectable. Furthermore, even with 10-fold lower precursor frequencies and a 16,000× lower affinity, nanoparticle immunogens outcompeted the monomer with respect to the quantity of detectable specific B cells 8 days post immunization [▪▪].Once in the lymph nodes, nanoparticles will bind more avidly to B-cell receptors (BCRs) on naive cognate B cells than soluble immunogens, enhancing BCR clustering, intracellular BCR-mediated signaling, and subsequent activation of the B cell. Indeed, several studies using Env nanoparticles and Env-specific B cells show that nanoparticle presentation enhances B-cell activation evision) ,20,21. Tevision) . Recentl) . We fused Env trimers to the trimeric I53-50A component, and subsequent mixing with the pentameric I53-50B component produced monodisperse and well ordered nanoparticles presenting twenty trimers (Env-I53-50NP). The assembly efficiency and Env quality were excellent .In vitro-assembling nanoparticle systems offer advantages as they allow more control over the quality of the Env trimers by allowing the purification of native-like trimers prior to assembly. The recent development of protein nanoparticles that self-assemble vaccines ,54. Bales . Within the competitive environment of the germinal center, it might also be critical to optimize presentation of bNAb epitopes and eliminate non-NAb epitopes. Therefore, the ability to purify native-like trimers prior to nanoparticle formation might be a critical advantage of liposomes and two-component self-assembling nanoparticles and make these particularly attractive platforms moving forward.Recent years have not only seen the emergence of new Env-nanoparticle platforms but also increased the understanding in the way nanoparticles enhance immunogenicity. These recent immunological insights as well as outcomes from immunization studies indicate that nanoparticles will be particularly beneficial during the priming immunization when the low affinity interactions of naïve B cells with immunogens benefit from enhanced avidity (Brouwer n press) ▪,23▪▪. WWe acknowledge Neil King for kindly sharing the Pymol script that was used to the create the Env trimer and nanoparticle structures inFig. Work by the authors in this area is supported by the U.S. National Institutes of Health Grant P01 AI110657; by the Bill and Melinda Gates Foundation through the Collaboration for AIDS Vaccine Discovery (CAVD), grants OPP1111923 and OPP1132237; by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 681137; and by a Vici grant from the Netherlands Organization for Scientific Research (NWO).There are no conflicts of interest.Papers of particular interest, published within the annual period of review, have been highlighted as:▪ of special interest▪▪ of outstanding interest