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Single Cell Genomics

Single cell sequencing technologies are revolutionizing cancer research by giving insight into cellular processes at an unprecedented level of detail. They overcome several problems of traditional bulk activity measurements such as requiring a substantial quantity of cells and high tumor purity. Single cell technologies enable the identification of cell types and dynamic states of individual cells in the tumor sample, leading to new insights in cancer biology, tumor composition, and treatment. The Máxima Single Cell Genomics Facility is a KiKa-funded research infrastructure that makes these technologies available to study pediatric tumors.

What we offer

Technologies

We facilitate both single-cell and single-nucleus RNA and DNA sequencing using three platforms. SORTseq is a technique for performing gene expression profiling of single cells that are FACS-sorted into 384-well plates. Mission Bio Tapestri offers single-cell DNA sequencing, allowing the identification of SNVs, indels, CNVs and other structural variants in individual cells. Lastly, the 10x Genomics platform allows a variety of  single-cell quantification strategies:

  • 3'- or 5’-end Gene Expression
  • Multiome: simultaneous gene expression and chromatin-accessibility per cell
  • Feature Barcoding: simultaneous gene expression and cell surface protein quantification
  • Cellplex: multiplexed gene expression, in which samples are pooled for higher throughput and reduced cost
  • V(D)J sequencing: simultaneous gene expression and T- and B-cell receptor sequencing
  • Visium: spatial transcriptomics, measuring the local gene expression in microscopic images

Services

The Single Cell Genomics facility makes single cell technologies available to researchers of the Princess Máxima Center. After an intake meeting where goals, feasibility and mutual expectations are discussed, we can provide:

  • Expertise, including advice on experimental design, planning and trouble shooting
  • Library preparation
  • Sequencing
  • Analysis and biological interpretation
  • Data management, including backup, live progress overviews, quality reports and downstream analyses
  • Sample multiplexing

Training

We train researchers on the required experimental procedures and equipment. We organize courses for bioinformaticians interested in analyzing their own data, both internally and abroad within the European network ELIXIR.

Computing

All data and metadata are collected and managed in comprehensive sample and library tracking database that allows us to adhere to the FAIR principles. We are involved in developing computational tools (Candelli et al. 2018; de Kanter et al. 2019) and make extensive use of high performance computing facilities at UMCU and cloud providers, using both traditional and virtualized environments.

Video
Watch facility video: https://youtu.be/EkOLDbyXIgQ


Our equipment:

  • 10x Genomics Chromium Controller
  • Missio Bio Tapestri platform
  • SPTLabtech Mosquito Genomics nanodispenser, 5-position, humidity-controlled deck
  • PCRmax Alpha4 Thermal Cycler
  • Dedicated UV HEPA PCR workstations for RNA and DNA work
  • Bio-Rad T100 Thermal Cycler
  • Bio-Rad C1000 Touch Thermal Cycler
  • Tecan D300e nanodispenser (shared with Drug Screening)
  • ThermoFisher Multidrop Combi dispenser (shared with Drug Screening)
  • Agilent Bioanalyzer
  • Perkin Elmer LabChip GX Touch HT nucleic acid analyzer

Personnel

Facility manager         Thanasis Margaritis

Bioinformaticians        Philip Lijnzaad
                                       Tito Candelli
                                       Lindy Visser

Wet lab                         Aleksandra Balwierz

Interested?

Belangrijke publicaties

2022

Candelli, T., Schneider, P., Garrido Castro, P., Jones, L.A., Bodewes, E., Rockx-Brouwer, D., Pieters, R., Holstege, F.C.P., Margaritis, T., Stam, R.W., (2022). Identification and characterization of relapse-initiating cells in MLL-rearranged infant ALL by single-cell transcriptomics. Leukemia 36, 58–67. https://doi.org/10.1038/s41375-021-01341-y PMID:34304246

Morris, V., Wang, D., Li, Z., Marion, W., Hughes, T., Sousa, P., Harada, T., Sui, S.H., Naumenko, S., Kalfon, J., Sensharma, P., Falchetti, M., Vinicius da Silva, R., Candelli, T., Schneider, P., Margaritis, T., Holstege, F.C.P., Pikman, Y., Harris, M., Stam, R.W., Orkin, S.H., Koehler, A.N., Shalek, A.K., North, T.E., Pimkin, M., Daley, G.Q., Lummertz da Rocha, E., Rowe, R.G., (2022). Hypoxic, glycolytic metabolism is a vulnerability of B-acute lymphoblastic leukemia-initiating cells. Cell Reports 39, 110752. https://doi.org/10.1016/j.celrep.2022.110752 PMID:35476984


202
1

Hanemaaijer, E.S., Margaritis, T., Sanders, K., Bos, F.L., Candelli, T., Al-Saati, H., van Noesel, M.M., Meyer-Wentrup, F.A.G., van de Wetering, M., Holstege, F.C.P., Clevers, H., (2021). Single-cell atlas of developing murine adrenal gland reveals relation of Schwann cell precursor signature to neuroblastoma phenotype. Proc Natl Acad Sci U S A 118, e2022350118. https://doi.org/10.1073/pnas.2022350118  PMID:33500353

van Ineveld, R.L., Margaritis, T., Kooiman, B.A.P., Groenveld, F., Ariese, H.C.R., Lijnzaad, P., Johnson, H.R., Korving, J., Wehrens, E.J., Holstege, F., van Rheenen, J., Drost, J., Rios, A.C., Bos, F.L., (2021). LGR6 marks nephron progenitor cells. Dev Dyn 250, 1568–1583. https://doi.org/10.1002/dvdy.346 PMID:33848015

Kildisiute, G., Kholosy, W.M., Young, M.D., Roberts, K., Elmentaite, R., Hooff, S.R. van, Pacyna, C.N., Khabirova, E., Piapi, A., Thevanesan, C., Bugallo-Blanco, E., Burke, C., Mamanova, L., Keller, K.M., Langenberg-Ververgaert, K.P.S., Lijnzaad, P., Margaritis, T., Holstege, F.C.P., Tas, M.L., Wijnen, M.H.W.A., Noesel, M.M. van, Valle, I. del, Barone, G., Linden, R. van der, Duncan, C., Anderson, J., Achermann, J.C., Haniffa, M., Teichmann, S.A., Rampling, D., Sebire, N.J., He, X., Krijger, R.R. de, Barker, R.A., Meyer, K.B., Bayraktar, O., Straathof, K., Molenaar, J.J., Behjati, S., (2021). Tumor to normal single-cell mRNA comparisons reveal a pan-neuroblastoma cancer cell. Science Advances 7, eabd3311. https://doi.org/10.1126/sciadv.abd3311 PMID:33547074

Young, M.D., Mitchell, T.J., Custers, L., Margaritis, T., Morales-Rodriguez, F., Kwakwa, K., Khabirova, E., Kildisiute, G., Oliver, T.R.W., de Krijger, R.R., van den Heuvel-Eibrink, M.M., Comitani, F., Piapi, A., Bugallo-Blanco, E., Thevanesan, C., Burke, C., Prigmore, E., Ambridge, K., Roberts, K., Braga, F.A.V., Coorens, T.H.H., Del Valle, I., Wilbrey-Clark, A., Mamanova, L., Stewart, G.D., Gnanapragasam, V.J., Rampling, D., Sebire, N., Coleman, N., Hook, L., Warren, A., Haniffa, M., Kool, M., Pfister, S.M., Achermann, J.C., He, X., Barker, R.A., Shlien, A., Bayraktar, O.A., Teichmann, S.A., Holstege, F.C., Meyer, K.B., Drost, J., Straathof, K., Behjati, S., (2021). Single cell derived mRNA signals across human kidney tumors. Nat Commun 12, 3896. https://doi.org/10.1038/s41467-021-23949-5 PMID:34162837


2020

Calandrini C, Schutgens F, Oka R, Margaritis T, Candelli T, Mathijsen L, Ammerlaan C, van Ineveld RL, Derakhshan S, de Haan S, Dolman E, Lijnzaad P, Custers L, Begthel H, Kerstens HHD, Visser LL, Rookmaaker M, Verhaar M, Tytgat GAM, Kemmeren P, de Krijger RR, Al-Saadi R, Pritchard-Jones K, Kool M, Rios AC, van den Heuvel-Eibrink MM, Molenaar JJ, van Boxtel R, Holstege FCP, Clevers H, Drost J. An organoid biobank for childhood kidney cancers that captures disease and tissue heterogeneity. (2020) Nature Communications, 11;11(1):1310. PMID: 32161258


2019

de Kanter JK, Lijnzaad P, Candelli T, Margaritis T, Holstege FCP. CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. (2019) Nucleic Acids Research, 47(16):e95. PMID: 31226206

Schutgens F, Rookmaaker MB, Margaritis T, Rios A, Ammerlaan C, Jansen J, Gijzen L, Vormann M, Vonk A, Viveen M, Yengej FY, Derakhshan S, de Winter-de Groot KM, Artegiani B, van Boxtel R, Cuppen E, Hendrickx APA, van den Heuvel-Eibrink MM, Heitzer E, Lanz H, Beekman J, Murk JL, Masereeuw R, Holstege F, Drost J, Verhaar MC, Clevers H. Tubuloids derived from human adult kidney and urine for personalized disease modeling. (2019) Nature Biotechnology, 37(3):303-313. PMID: 30833775


2018

Candelli T, Lijnzaad P, Muraro MJ, van Oudenaarden A, Margaritis T, Holstege F. Sharq, a Versatile Preprocessing and QC Pipeline for Single Cell RNA-Seq. (2018) BioRxiv. doi: https://doi.org/10.1101/250811