Bio

Professional Education


  • Doctor of Philosophy, Karolinska Institutet, Single cell genomics (2013)
  • Master of Science, Uppsala University, Sweden, Molecular cell Biology (2008)

Stanford Advisors


Research & Scholarship

Current Research and Scholarly Interests


Saiful has completed his PhD at the Molecular Neurobiology division in the Department of Medical Biochemistry and Biophysics of the Karolinska Institutet, Sweden. He has a strong background in molecular biology and is highly experienced in method development. Single-cell transcriptomics is his key area of interest. Saiful developed two methods in the single-cell field; Single-cell Tagged Reverse Transcription (STRT) and molecule counting at single cell level using Unique Molecular Identifier (UMI). One of the goals of his research is to identify the cell types in a complex tissue ? a fundamental question in cell biology. In his postdoctoral work at Stanford, Saiful is applying single cell transcriptomics methods to the field of immunological self-tolerance in human. In addition, Saiful is also involved in screening for chemical and genetic strategies to improve a rare genetic disease caused by a defect in N-glycanase and associated with abnormal accumulation of misfolded glycoproteins.

Publications

All Publications


  • Rewired metabolism in drug-resistant leukemia cells: A metabolic switch hallmarked by reduced dependence on exogenous glutamine The Journal of Biological Chemistry Staubert, C. 2015

    View details for DOI 10.1074/jbc.M114.618769

  • Quantitative single-cell RNA-seq with unique molecular identifiers NATURE METHODS Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lonnerberg, P., Linnarsson, S. 2014; 11 (2): 163-?

    Abstract

    Single-cell RNA sequencing (RNA-seq) is a powerful tool to reveal cellular heterogeneity, discover new cell types and characterize tumor microevolution. However, losses in cDNA synthesis and bias in cDNA amplification lead to severe quantitative errors. We show that molecular labels--random sequences that label individual molecules--can nearly eliminate amplification noise, and that microfluidic sample preparation and optimized reagents produce a fivefold improvement in mRNA capture efficiency.

    View details for DOI 10.1038/NMETH.2772

    View details for Web of Science ID 000331141600017

    View details for PubMedID 24363023

  • Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing Nature Neuroscience Usoskin, D., et al 2014

    View details for DOI 10.1038/nn.3881

  • Base Preferences in Non-Templated Nucleotide Incorporation by MMLV-Derived Reverse Transcriptases PLOS ONE Zajac, P., Islam, S., Hochgerner, H., Lonnerberg, P., Linnarsson, S. 2013; 8 (12)

    Abstract

    Reverse transcriptases derived from Moloney Murine Leukemia Virus (MMLV) have an intrinsic terminal transferase activity, which causes the addition of a few non-templated nucleotides at the 3' end of cDNA, with a preference for cytosine. This mechanism can be exploited to make the reverse transcriptase switch template from the RNA molecule to a secondary oligonucleotide during first-strand cDNA synthesis, and thereby to introduce arbitrary barcode or adaptor sequences in the cDNA. Because the mechanism is relatively efficient and occurs in a single reaction, it has recently found use in several protocols for single-cell RNA sequencing. However, the base preference of the terminal transferase activity is not known in detail, which may lead to inefficiencies in template switching when starting from tiny amounts of mRNA. Here, we used fully degenerate oligos to determine the exact base preference at the template switching site up to a distance of ten nucleotides. We found a strong preference for guanosine at the first non-templated nucleotide, with a greatly reduced bias at progressively more distant positions. Based on this result, and a number of careful optimizations, we report conditions for efficient template switching for cDNA amplification from single cells.

    View details for DOI 10.1371/journal.pone.0085270

    View details for Web of Science ID 000329323900145

    View details for PubMedID 24392002

  • RNA-Seq Analysis Reveals Different Dynamics of Differentiation of Human Dermis- and Adipose-Derived Stromal Stem Cells PLOS ONE Jaeaeger, K., Islam, S., Zajac, P., Linnarsson, S., Neuman, T. 2012; 7 (6)

    Abstract

    Tissue regeneration and recovery in the adult body depends on self-renewal and differentiation of stem and progenitor cells. Mesenchymal stem cells (MSCs) that have the ability to differentiate into various cell types, have been isolated from the stromal fraction of virtually all tissues. However, little is known about the true identity of MSCs. MSC populations exhibit great tissue-, location- and patient-specific variation in gene expression and are heterogeneous in cell composition.Our aim was to analyze the dynamics of differentiation of two closely related stromal cell types, adipose tissue-derived MSCs (AdMSCs) and dermal fibroblasts (FBs) along adipogenic, osteogenic and chondrogenic lineages using multiplex RNA-seq technology. We found that undifferentiated donor-matched AdMSCs and FBs are distinct populations that stay different upon differentiation into adipocytes, osteoblasts and chondrocytes. The changes in lineage-specific gene expression occur early in differentiation and persist over time in both AdMSCs and FBs. Further, AdMSCs and FBs exhibit similar dynamics of adipogenic and osteogenic differentiation but different dynamics of chondrogenic differentiation.Our findings suggest that stromal stem cells including AdMSCs and dermal FBs exploit different molecular mechanisms of differentiation to reach a common cell fate. The early mechanisms of differentiation are lineage-specific and are similar for adipogenic and osteogenic differentiation but are distinct for chondrogenic differentiation between AdMSCs and FBs.

    View details for DOI 10.1371/journal.pone.0038833

    View details for Web of Science ID 000305652700033

    View details for PubMedID 22723894

  • Highly multiplexed and strand-specific single-cell RNA 5 ' end sequencing NATURE PROTOCOLS Islam, S., Kjallquist, U., Moliner, A., Zajac, P., Fan, J., Lonnerberg, P., Linnarsson, S. 2012; 7 (5): 813-828

    Abstract

    Single-cell analysis of gene expression is increasingly important for the analysis of complex tissues, including cancer, developing organs and adult stem cell niches. Here we present a detailed protocol for quantitative gene expression analysis in single cells, by the sequencing of mRNA 5' ends. In all, 96 cells are lysed, and their mRNA is converted to cDNA. By using a template-switching mechanism, a bar code and an upstream primer-binding sequence are introduced simultaneously with reverse transcription. All cDNA is pooled and then prepared for 5' end sequencing, including fragmentation, adapter ligation and PCR amplification. The chief advantage of this approach is the great reduction in cost and time, afforded by the early bar-coding strategy. Compared with previous methods, it is more suitable for large-scale quantitative analysis, as well as for the characterization of transcription start sites, but it is unsuitable for the detection of alternatively spliced transcripts. Sample preparation takes 3 d, and two sets of 96 cells can be prepared in parallel. Finally, the sequencing and data analysis can take an additional 4 d altogether.

    View details for DOI 10.1038/nprot.2012.022

    View details for Web of Science ID 000303359300001

    View details for PubMedID 22481528

  • Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq GENOME RESEARCH Islam, S., Kjallquist, U., Moliner, A., Zajac, P., Fan, J., Lonnerberg, P., Linnarsson, S. 2011; 21 (7): 1160-1167

    Abstract

    Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves-all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease.

    View details for DOI 10.1101/gr.110882.110

    View details for Web of Science ID 000292298000016

    View details for PubMedID 21543516

  • Genetic analysis of single cells Proceedings of the 101st Annual Meeting of the American Association for Cancer Research Fan, J. 2010: 1149

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