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Mermaid renders literal \n as text on GitHub — line breaks inside node labels require <br/> syntax. Fixed 12 occurrences across 4 files: - SKILL.md: three-phase workflow (Phase 1/2/3 nodes) - issue-00000001: three-phase workflow nodes - pr-00000001: skill name node - example-research-report.md: Stage 1-5 nodes in experimental workflow
219 lines
8.7 KiB
Markdown
219 lines
8.7 KiB
Markdown
# CRISPR-Based Gene Editing Efficiency Analysis
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_Example research report — demonstrates markdown-mermaid-writing skill standards. All diagrams use Mermaid embedded in markdown as the source format._
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---
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## 📋 Overview
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This report analyzes the efficiency of CRISPR-Cas9 gene editing across three cell line models under variable guide RNA (gRNA) conditions. Editing efficiency was quantified by T7E1 assay and next-generation sequencing (NGS) of on-target loci[^1].
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**Key findings:**
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- HEK293T cells show highest editing efficiency (mean 78%) across all gRNA designs
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- GC content between 40–65% correlates with editing efficiency (r = 0.82)
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- Off-target events occur at <0.1% frequency across all conditions tested
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---
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## 🔄 Experimental workflow
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CRISPR editing experiments followed a standardized five-stage protocol. Each stage has defined go/no-go criteria before proceeding.
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```mermaid
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flowchart TD
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accTitle: CRISPR Editing Experimental Workflow
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accDescr: Five-stage experimental pipeline from gRNA design through data analysis, with quality checkpoints between each stage.
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design["🧬 Stage 1<br/>gRNA Design<br/>(CRISPRscan + Cas-OFFinder)"]
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synth["⚙️ Stage 2<br/>Oligo Synthesis<br/>& Annealing"]
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transfect["🔬 Stage 3<br/>Cell Transfection<br/>(Lipofectamine 3000)"]
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screen["🧪 Stage 4<br/>Primary Screen<br/>(T7E1 assay)"]
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ngs["📊 Stage 5<br/>NGS Validation<br/>(150 bp PE reads)"]
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qc1{GC 40-65%?}
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qc2{Yield ≥ 2 µg?}
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qc3{Viability ≥ 85%?}
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qc4{Band visible?}
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design --> qc1
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qc1 -->|"✅ Pass"| synth
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qc1 -->|"❌ Redesign"| design
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synth --> qc2
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qc2 -->|"✅ Pass"| transfect
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qc2 -->|"❌ Re-synthesize"| synth
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transfect --> qc3
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qc3 -->|"✅ Pass"| screen
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qc3 -->|"❌ Optimize"| transfect
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screen --> qc4
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qc4 -->|"✅ Pass"| ngs
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qc4 -->|"❌ Repeat"| screen
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classDef stage fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#1e3a5f
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classDef gate fill:#fef9c3,stroke:#ca8a04,stroke-width:2px,color:#713f12
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classDef fail fill:#fee2e2,stroke:#dc2626,stroke-width:2px,color:#7f1d1d
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class design,synth,transfect,screen,ngs stage
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class qc1,qc2,qc3,qc4 gate
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```
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---
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## 🔬 Methods
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### Cell lines and culture
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Three cell lines were used: HEK293T (human embryonic kidney), K562 (chronic myelogenous leukemia), and Jurkat (T-lymphocyte). All lines were maintained in RPMI-1640 with 10% FBS at 37°C / 5% CO₂[^2].
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### gRNA design and efficiency prediction
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gRNAs targeting the _EMX1_ locus were designed using CRISPRscan[^3] with the following criteria:
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| Criterion | Threshold | Rationale |
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| -------------------- | --------- | ------------------------------------- |
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| GC content | 40–65% | Optimal Tm and Cas9 binding |
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| CRISPRscan score | ≥ 0.6 | Predicted on-target activity |
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| Off-target sites | ≤ 5 (≤3 mismatches) | Reduce off-target editing risk |
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| Homopolymer runs | None (>4 nt) | Prevents premature transcription stop |
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### Transfection protocol
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RNP complexes were assembled at 1:1.2 molar ratio (Cas9:gRNA) and delivered by lipofection. Cells were harvested 72 hours post-transfection for genomic DNA extraction.
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### Analysis pipeline
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```mermaid
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sequenceDiagram
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accTitle: NGS Data Analysis Pipeline
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accDescr: Sequence of computational steps from raw FASTQ files through variant calling to final efficiency report.
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participant raw as 📥 Raw FASTQ
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participant qc as 🔍 FastQC
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participant trim as ✂️ Trimmomatic
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participant align as 🗺️ BWA-MEM2
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participant call as ⚙️ CRISPResso2
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participant report as 📊 Report
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raw->>qc: Per-base quality scores
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qc-->>trim: Flag low-Q reads (Q<20)
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trim->>align: Cleaned reads
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align->>align: Index reference genome (hg38)
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align->>call: BAM + target region BED
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call->>call: Quantify indel frequency
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call-->>report: Editing efficiency (%)
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call-->>report: Off-target events
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report-->>report: Statistical summary
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```
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---
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## 📊 Results
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### Editing efficiency by cell line
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| Cell line | n (replicates) | Mean efficiency (%) | SD (%) | Range (%) |
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| ---------- | -------------- | ------------------- | ------ | --------- |
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| **HEK293T** | 6 | **78.4** | 4.2 | 71.2–84.6 |
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| K562 | 6 | 52.1 | 8.7 | 38.4–63.2 |
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| Jurkat | 6 | 31.8 | 11.3 | 14.2–47.5 |
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HEK293T cells showed significantly higher editing efficiency than both K562 (p < 0.001) and Jurkat (p < 0.001) lines by one-way ANOVA with Tukey post-hoc correction.
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### Effect of GC content on efficiency
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GC content between 40–65% was strongly correlated with editing efficiency (Pearson r = 0.82, p < 0.0001, n = 48 gRNAs).
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```mermaid
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xychart-beta
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accTitle: Editing Efficiency vs gRNA GC Content
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accDescr: Bar chart showing mean editing efficiency grouped by GC content bins, demonstrating optimal performance in the 40 to 65 percent GC range
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title "Mean Editing Efficiency by GC Content Bin (HEK293T)"
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x-axis ["< 30%", "30–40%", "40–50%", "50–65%", "> 65%"]
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y-axis "Editing Efficiency (%)" 0 --> 100
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bar [18, 42, 76, 81, 38]
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```
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### Timeline of key experimental milestones
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```mermaid
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timeline
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accTitle: Experiment Timeline — CRISPR Efficiency Study
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accDescr: Chronological milestones from study design through manuscript submission across six months
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section Month 1
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Study design and gRNA library design : 48 gRNAs across 3 target loci
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Cell line authentication : STR profiling confirmed all three lines
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section Month 2
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gRNA synthesis and QC : 46/48 gRNAs passed yield threshold
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Pilot transfections (HEK293T) : Optimized lipofection conditions
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section Month 3
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Full transfection series : All 3 cell lines, all 46 gRNAs, 6 replicates
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T7E1 primary screening : Passed go/no-go for all conditions
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section Month 4
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NGS library preparation : 276 samples processed
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Sequencing run (NovaSeq) : 150 bp PE, mean 50k reads/sample
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section Month 5
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Bioinformatic analysis : CRISPResso2 pipeline
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Statistical analysis : ANOVA, correlation, regression
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section Month 6
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Manuscript preparation : This report
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```
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---
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## 🔍 Discussion
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### Why HEK293T outperforms suspension lines
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HEK293T's superior editing efficiency relative to K562 and Jurkat likely reflects three factors[^4]:
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1. **Adherent morphology** — enables more uniform lipofection contact
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2. **High transfection permissiveness** — HEK293T expresses the SV40 large T antigen, which may facilitate nuclear import
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3. **Cell cycle distribution** — higher proportion in S/G2 phase where HDR is favored
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<details>
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<summary><strong>🔧 Technical details — off-target analysis</strong></summary>
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Off-target editing was assessed by GUIDE-seq at the 5 highest-activity gRNAs. No off-target sites exceeding 0.1% editing frequency were detected. The three potential sites flagged by Cas-OFFinder (≤2 mismatches) showed 0.00%, 0.02%, and 0.04% indel frequencies — all below the assay noise floor of 0.05%.
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Full GUIDE-seq data available in supplementary data package (GEO accession pending).
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</details>
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---
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### Comparison with published benchmarks
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```mermaid
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radar
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accTitle: CRISPR Method Comparison Radar
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accDescr: Multi-dimensional radar chart comparing our protocol against published Cas9 and base editing benchmarks across five performance axes
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title Performance vs. Published Methods
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x-axis ["Efficiency", "Specificity", "Delivery ease", "Cost", "Cell viability"]
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"This study (RNP + Lipo)" : [78, 95, 80, 85, 90]
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"Plasmid Cas9 (lit.)" : [55, 70, 90, 95, 75]
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"Electroporation RNP (lit.)" : [88, 96, 50, 60, 65]
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```
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---
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## 🎯 Conclusions
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1. RNP-lipofection in HEK293T achieves >75% CRISPR editing efficiency — competitive with electroporation without the associated viability cost
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2. gRNA GC content is the single strongest predictor of editing efficiency in our dataset (r = 0.82)
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3. This protocol is not directly transferable to suspension lines without further optimization; K562 and Jurkat require electroporation or viral delivery for comparable efficiency
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---
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## 🔗 References
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[^1]: Ran, F.A. et al. (2013). "Genome engineering using the CRISPR-Cas9 system." _Nature Protocols_, 8(11), 2281–2308. https://doi.org/10.1038/nprot.2013.143
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[^2]: ATCC. (2024). "Cell Line Authentication and Quality Control." https://www.atcc.org/resources/technical-documents/cell-line-authentication
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[^3]: Moreno-Mateos, M.A. et al. (2015). "CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo." _Nature Methods_, 12(10), 982–988. https://doi.org/10.1038/nmeth.3543
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[^4]: Molla, K.A. & Yang, Y. (2019). "CRISPR/Cas-Mediated Base Editing: Technical Considerations and Practical Applications." _Trends in Biotechnology_, 37(10), 1121–1142. https://doi.org/10.1016/j.tibtech.2019.03.008
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