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RNA sequencing and swarm intelligence–enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA

Nature Protocols volume 14, pages 1206–1234 (2019)Cite this article

Abstract

Blood-based diagnostics tests, using individual or panels of biomarkers, may revolutionize disease diagnostics and enable minimally invasive therapy monitoring. However, selection of the most relevant biomarkers from liquid biosources remains an immense challenge. We recently presented the thromboSeq pipeline, which enables RNA sequencing and cancer classification via self-learning and swarm intelligence–enhanced bioinformatics algorithms using blood platelet RNA. Here, we provide the wet-lab protocol for the generation of platelet RNA-sequencing libraries and the dry-lab protocol for the development of swarm intelligence–enhanced machine-learning-based classification algorithms. The wet-lab protocol includes platelet RNA isolation, mRNA amplification, and preparation for next-generation sequencing. The dry-lab protocol describes the automated FASTQ file pre-processing to quantified gene counts, quality controls, data normalization and correction, and swarm intelligence–enhanced support vector machine (SVM) algorithm development. This protocol enables platelet RNA profiling from 500 pg of platelet RNA and allows automated and optimized biomarker panel selection. The wet-lab protocol can be performed in 5 d before sequencing, and the algorithm development can be completed in 2 d, depending on computational resources. The protocol requires basic molecular biology skills and a basic understanding of Linux and R. In all, with this protocol, we aim to enable the scientific community to test platelet RNA for diagnostic algorithm development.

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Fig. 1: Overview of PSO-enhanced thromboSeq.
Fig. 2: Pre-analytical variables for platelet processing.
Fig. 3: Platelet total RNA and SMARTer cDNA quality assessment.
Fig. 4: SMARTer cDNA amplification and TruSeq labeling.
Fig. 5: Comparison of single-end read versus paired-end read sequencing.
Fig. 6: Progress plots of the PSO-enhanced thromboSeq algorithm.
Fig. 7: PSO-enhanced thromboSeq for lower-grade glioma diagnostics.

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Code availability

The thromboSeq dry-lab source code is available via GitHub (https://github.com/MyronBest/thromboSeq_source_code), and is for research purposes only.

Data availability

The HD-LGG raw sequencing data FASTQ files have been deposited in the NCBI GEO database under accession no. GSE 107868. The non-cancer NSCLC dataset is available at GSE 89843.

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Acknowledgements

Financial support was provided by European Research Council grants 713727 and 336540 (T.W.), Dutch Organisation of Scientific Research grant 91711366 (T.W.), the Dutch Cancer Society (T.W.), Horizon 2020 Marie-Curie European Liquid Biopsy Academy grant 765492 (T.W.), and Stichting STOPHersentumoren.nl (M.G.B., N.S., T.W.). We are thankful to F. Rustenburg, H. Verschueren, E. Post, T. Lagerweij, P. Schellen, L.E. Wedekind, I.E. Kooi, D. Vessies, D. van den Broek, B. Ylstra, J.C. Reijneveld, D.P. Noske, W.P. Vandertop, and P. Wesseling for their contributions. We thank R.J.A. Nilsson, L. Köhn, M. Arkani, and C. Oudejans for testing the thromboSeq software.

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Authors and Affiliations

  1. Department of Neurosurgery, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands

    Myron G. Best, Sjors G. J. G. In ’t Veld & Thomas Wurdinger

  2. Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands

    Myron G. Best

  3. Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands

    Myron G. Best, Sjors G. J. G. In ’t Veld, Nik Sol & Thomas Wurdinger

  4. Department of Neurology, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands

    Nik Sol

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Contributions

M.G.B. and T.W. designed the thromboSeq pipeline and wrote the manuscript. S.G.J.G.I.t.V. and N.S. contributed to the dry- and wet-lab protocol design.

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Correspondence to Myron G. Best or Thomas Wurdinger.

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Competing interests

M.G.B. and T.W. are inventors on relevant patent applications. T.W. received funding from Illumina and is a shareholder of GRAIL, Inc.

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Key references that use this protocol

Best, M. G. et al. Cancer Cell 28, 666–676 (2015): https://doi.org/10.1016/j.ccell.2015.09.018

Best, M. G. et al. Cancer Cell 32, 238.e9–252.e9 (2017): https://doi.org/10.1016/j.ccell.2017.07.004

Integrated supplementary information

Supplementary Figure 1 CD45 depletion of platelet preparations processed according to the thromboSeq protocol.

(A) Summary of flow cytometry experiment indicating the number of nucleated cells and platelets detected in platelet preparations (n=3 healthy individuals) isolated with and without a CD45-depletion step (EasySep, StemCell Technologies, #29037). Samples were stained with anti-human CD42b-FITC (Beckman Coulter, IM0648U) and the DNA-marker TOTO-3 (Thermo Fisher Scientific), and quantified using the BD LSRFortessa X-20. Based on the FSC/SSC-gating cellular and platelet fractions were identified. Nucleated cells were distinguished by TOTO-3 DNA positivity. Though CD45 depletion results in no nucleated cells detected, the number of identified platelets was reduced significantly. (B) Representative Agilent Bioanalyzer Picochip analysis of platelet preparations without (left) and with (right) an EasySep CD45-depletion step. CD45 depletion results in reduced platelet and platelet total RNA yield. (C) Read counts per one million total spliced reads mapping to the CD45 gene in both the HD-LGG dataset and publicly available non-cancer versus NSCLC dataset. This data indicates that remaining CD45 transcripts can be detected in the thromboSeq FASTQ-files.

Supplementary Figure 2 Examples of Agilent Bioanalyzer traces.

Shown are representative examples of Agilent Bioanalyzer traces related to the section. (A) Incorrect marker recognized by Agilent Bioanalyzer software. The 5S peak was detected as the reference marker due to total RNA overload (left). Dilution of the total RNA in nuclease-free H2O resulted in a high-quality RNA Bioanalyzer trace (right). (B) Appearance of a degraded RNA profile due to Picochip overload. The Agilent Bioanalyzer RNA Picochip was overloaded with total RNA, resulting in a Bioanalyzer trace similar to that of samples with degraded RNA (left). Notice the reference marker shows low fluorescence signal. Dilution of the sample in nuclease-free H2O resulted in a high-quality RNA Bioanalyzer trace (right). (C) Skewed Agilent Bioanalyzer profiles. Gel electrophoresis traces of skewed profiles as obtained from the Agilent Bioanalyzer software, likely due to pin contamination (left). Gel electrophoresis traces of a successfully analyzed Bioanalyzer RNA Picochip. (D) Incorrect marker recognized by Agilent Bioanalyzer software. An incorrect peak of the Bioanalyzer RNA Picochip trace was selected as reference marker (left). Selection of the correct peak as the reference marker results in correct quantification of the total RNA isolate.

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Best, M.G., In ’t Veld, S.G.J.G., Sol, N. et al. RNA sequencing and swarm intelligence–enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA. Nat Protoc 14, 1206–1234 (2019). https://doi.org/10.1038/s41596-019-0139-5

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