SICOF

The Single Cell Core Facility for Flemingsberg campus (SICOF) is a state of the art core facility, offering services to the research community. Our goal is to facilitate the use of single cell/nuclei sequencing technologies for all researchers.

How to book our services

To request or book a service from SICOF, log in via iLab.
Here you can also find the SICOF manual and instructions for the iLab system.

The team

The team has abundant experience with single cell technologies since the inception of SICOF in 2016 as an in-house facility for the Integrated Cardio Metabolic Institute (ICMC). We aim to provide all researchers with clustered data that can be interpreted and analyzed without the need for a bioinformatician.

Photo of six people standing next to each other.
The SICOF team, from left: Michael Vanlandewijck, Elisabeth Raschperger, Jianping Liu, Byambajav Buyandelger, Sonja Gustafsson, Giuseppe Mocci. Photo: Stefan Zimmerman

Head of facility: Michael Vanlandewijck

Administrator: Elisabeth Raschperger

Research engineers:

Bioinformatician: Giuseppe Mocci

Contact us

SICOF is hosted by the Department of Medicine, Huddinge (MedH).

singlecellseq@ki.se

Phone: 08 - 524 834 18​​

Visiting address

Karolinska Institutet | MedH
Neo, floor 7 (room 7333)
Blickagången 16
KI Campus Flemingsberg  

Delivery addess

SICOF Room 7333
Karolinska Institutet | Institutionen för medicin, Huddinge
Hälsovägen 7C
Neo, floor 8
141 57 Huddinge

Collaborations

We work in close collaboration with:

Our services

FACS sorting   

SICOF houses a BD Melody sorter for single cell sorts. Although we do offer single cell sorting support, we would like to refer you in the first place to the MedH Flow Cytometry Core Facility, which is cheaper and has more dedicated support for your flow cytometry and single cell sorts.  

Smart-seq2  

Developed in 2014 by the Sandberg lab at KI, the Smart-seq2 chemistry paved the way for single cell RNA sequencing. By using the SMART technique of template switch, full-length sequencing of the transcriptome became possible on a single cell level, enabling detection of thousands of genes per cell (2500-6000 depending on cell type).

Smart-seq2 is currently being phased out for Smart-seq3.We can still offer Smart-seq2 as a service, but sequencing of Smart-seq2 libraries will be performed in another format at BEA, potentially with an increase of cost.

Smart-seq3  

The Smart-seq3 chemistry is an evolution of Smart-seq2, offering increased depth (3500-9000 genes/cell, depending on cell type) while also introducing UMI based gene counting for more accurate gene expression counts. The full-length information is also preserved, and less starting material is required. The cost is also lower compared to Smart-seq2.   

Small RNA-seq  

Small RNA-seq is another adaptation of the SMART chemistry, focusing on the study of the small RNA’s in a single cell. This is mostly miRNA’s, but may also include tRNA’s and snoRNA’s. Abundant ribosomal RNA’s are masked so that they don’t overwhelm the sequencing data.  

Bulk RNA-seq in single cell format via Smart-seq3 & Smart-seq2 chemistry​ 

We use SMART chemistry to study bulk cells/organisms in a high-throughput manner. Cells or small tissues are lysed in a 384 well plate and the lysates are then processed as single cells, but with adjustment for the higher starting material.

Right now, this method is optimized for sets of 384 samples, but SICOF is working on methods to scale this down to study smaller sample numbers.  

10x genomics-based droplet sequencing  

In 2015, the single cell field was revolutionized by the introduction of droplet sequencing, in other words the encapsulation of cells and reagents into small droplets for library preparation. This dramatically increased the throughput of single cell sequencing while reducing the cost per cell. This approach is commercialized by several companies, and we offer their entire portfolio of single cell solutions from 10x Genomics.

Small molecule screening

Bioinformatic analysis   

The analysis of single cell data is challenging. We therefore offer several levels of data analysis, ranging from simple QC to customized online database constructions, where you can browse your data in an accessible interface. For our analysis, we use our own DAITARN pipeline, a highly customized integration of several published algorithms such as Seurat, FastQC, Pagoda2, Cellranger, SPIN, and many others.

For coding language, we try to use Python as much as possible due to its speed and memory efficiency, but we also use R script, Html and CSS. SICOF also houses its own computing server with 56 CPU cores, 512 GB memory and over 300 TB of storage space.  

Quality Control 

Good single cell data starts with high quality cells. We therefore focus a lot on Quality Control (QC) before proceeding with single cell sequencing. We house several instruments for QC, such as three Bioanalyzers, a Fragment Analyzer and a LUNA FL cell counter. We use these instruments to assess your cells and/or cDNA quality prior to single cell sequencing.  

Liquid handling 

We use several robots which we can also use for custom high throughput liquid handling outside of our single cell pipeline, depending on feasibility and time of the SICOF staff. One example includes the dispensing of molecule libraries using the acoustic tip-free Echo dispenser.

Services under development

Note! These services are under implementation. Their availability as a service is dependent on the workload of SICOF and therefore, implementation takes an undefined length of time. In case of a strong desire of the single cell user base for a technology to be implemented, we will prioritise its implementation.   

Single cell fractionations

This technology uses Smart-seq3 on the cytoplasm for mRNA transcriptional profiling and preserves the nucleus for different analysis; whole genome sequencing (DNTR-Seq), chromatin analysis (ATAC-Smart3) and targeted mutational analysis (Target-seq).

Spatial transcriptomics

This technology attempts to preserve the anatomical information when studying single cells. We are keeping a close eye on the solutions of 10x Genomics, such as Visium for spatial spot analysis in fresh or fixed tissues, and Xenium multiplex in situ hybridisation and visualisation of gene expression. A big challenge using Visium is the transfer of the tissue to the Visium slide.

We are considering to acquire the CytAssist instrument, to be able to offer this as a service. The Xenium is a high-cost investment and we will pursue this, provided that the SICOF user base has a strong interest in this service.

Instruments

At SICOF we use the following instruments to conduct the services provided.

FACS sorter

FACSMelody™ Automated Cell Sorter BD

Liquid handler

  • Bravo Automated Liquid Handling Platform I Agilent
  • Echo 525 Acoustic Liquid Handler | Beckman Coulter
  • VIAFLO 96 and VIAFLO 384 - Electronic Pipettes | Integra

Detection & Quantification

  • 2100 Bioanalyzer | Agilent
  • Fragment Analyzer Systems Capillary Array | Agilent
  • Qubit Fluorometric Quantification | Thermo Fisher Scientific
  • LUNA-FL™ Dual Fluorescence Cell Counter | Logos

Thermal cycler

  • 14 Thermal Cyclers for PCR | Bio-Rad

Chromium Single Cell Platform

Database

For every step of analysis chosen, we offer the hosting of your data in an online, browsable database which is shareable with collaborators. Every database instance has a yearly cost to to keep online, starting one year after creation. This service can also be used for bulk data. In this case, we provide a list of DEG's for you.

At this stage, we don't offer the option to host databases of bulk-seq data.

Recent user publications 

2022

A single-cell transcriptomic inventory of murine smooth muscle cells.
Lars Muhl, Giuseppe Mocci, Riikka Pietilä, Jianping Liu, Liqun He, Guillem Genové, Stefanos Leptidis, Sonja Gustafsson, Byambajav Buyandelger, Elisabeth Raschperger, Emil M. Hansson, Johan L.M. Björkegren, Michael Vanlandewijck, Urban Lendahl & Christer Betsholtz.  Developmental Cell, 57(20), 24 October 2022, Pages 2426-2443.e6. doi: 10.1016/j.devcel.2022.09.015.

KCNJ8/ABCC9-containing K-ATP channel modulates brain vascular smooth muscle development and neurovascular coupling.
Ando K, Tong L, Peng D, Vázquez-Liébanas E, Chiyoda H, He L, Liu J, Kawakami K, Mochizuki N, Fukuhara S, Grutzendler J, Betsholtz C
Dev Cell 2022 Jun;57(11):1383-1399.e7. doi.org/10.1016/j.devcel.2022.04.019  

Loss of vascular endothelial notch signaling promotes spontaneous formation of tertiary lymphoid structures.
Fleig S, Kapanadze T, Bernier-Latmani J, Lill JK, Wyss T, Gamrekelashvili J, Kijas D, Liu B, Hüsing AM, Bovay E, Jirmo AC, Halle S, Ricke-Hoch M, Adams RH, Engel DR, von Vietinghoff S, Förster R, Hilfiker-Kleiner D, Haller H, Petrova TV, Limbourg FP
Nat Commun 2022 04;13(1):2022. doi.org/10.1038/s41467-022-29701-x

The SARS-CoV-2 receptor ACE2 is expressed in mouse pericytes but not endothelial cells: Implications for COVID-19 vascular research.
Muhl L, He L, Sun Y, Andaloussi Mäe M, Pietilä R, Liu J, Genové G, Zhang L, Xie Y, Leptidis S, Mocci G, Stritt S, Osman A, Anisimov A, Hemanthakumar KA, Räsänen M, Hansson EM, Björkegren J, Vanlandewijck M, Blomgren K, Mäkinen T, Peng XR, Hu Y, Ernfors P, Arnold TD, Alitalo K, Lendahl U, Betsholtz C
Stem Cell Reports 2022 05;17(5):1089-1104. doi.org/10.1016/j.stemcr.2022.03.016

2021

A human cell type similar to murine central nervous system perivascular fibroblasts-like cells.
Liu J, He L, Muhl L, Mocci G, Gustavsson S, Buyandelger B, Vanlandewijck M, Betsholtz C, Westermark B, Andrae J
Exp Cell Res 2021 05;402(2):112576. doi.org/10.1016/j.yexcr.2021.112576

Single-cell RNA sequencing reveals the mesangial identity and species diversity of glomerular cell transcriptomes.
He B, Chen P, Zambrano S, Dabaghie D, Hu Y, Möller-Hackbarth K, Unnersjö-Jess D, Korkut GG, Charrin E, Jeansson M, Bintanel-Morcillo M, Witasp A, Wennberg L, Wernerson A, Schermer B, Benzing T, Ernfors P, Betsholtz C, Lal M, Sandberg R, Patrakka J
Nat Commun 2021 04;12(1):2141. doi.org/10.1038/s41467-021-22331-9

Single-Cell Analysis of Blood-Brain Barrier Response to Pericyte Loss.
Mäe MA, He L, Nordling S, Vazquez-Liebanas E, Nahar K, Jung B, Li X, Tan BC, Chin Foo J, Cazenave-Gassiot A, Wenk MR, Zarb Y, Lavina B, Quaggin SE, Jeansson M, Gu C, Silver DL, Vanlandewijck M, Butcher EC, Keller A, Betsholtz C
Circ Res 2021 02;128(4):e46-e62. doi.org/10.1161/CIRCRESAHA.120.317473

2020

Single-cell analysis uncovers fibroblast heterogeneity and criteria for fibroblast and mural cell identification and discrimination.
Muhl L, Genové G, Leptidis S, Liu J, He L, Mocci G, Sun Y, Gustafsson S, Buyandelger B, Chivukula IV, Segerstolpe Å, Raschperger E, Hansson EM, Björkegren JLM, Peng XR, Vanlandewijck M, Lendahl U, Betsholtz C. Nat Commun 2020 08;11(1):3953. doi.org/10.1038/s41467-020-17740-1

Single-cell RNA counting at allele and isoform resolution using Smart-seq3.
Hagemann-Jensen M, Ziegenhain C, Chen P, Ramsköld D, Hendriks GJ, Larsson AJM, Faridani OR, Sandberg R
Nat Biotechnol 2020 06;38(6):708-714. doi.org/10.1038/s41587-020-0497-0

DAF-16/FOXO requires Protein Phosphatase 4 to initiate transcription of stress resistance and longevity promoting genes.
Sen I, Zhou X, Chernobrovkin A, Puerta-Cavanzo N, Kanno T, Salignon J, Stoehr A, Lin XX, Baskaner B, Brandenburg S, Björkegren C, Zubarev RA, Riedel CG
Nat Commun 2020 01;11(1):138. doi.org/10.1038/s41467-019-13931-7

2019

Adipose lipid turnover and long-term changes in body weight.
Arner P, Bernard S, Appelsved L, Fu KY, Andersson DP, Salehpour M, Thorell A, Rydén M, Spalding KL
Nat Med 2019 09;25(9):1385-1389. doi.org/10.1038/s41591-019-0565-5