Defining cell types and states with single-cell genomics

Abstract

A revolution in cellular measurement technology is under way: For the first time, we have the ability to monitor global gene regulation in thousands of individual cells in a single experiment. Such experiments will allow us to discover new cell types and states and trace their developmental origins. They overcome fundamental limitations inherent in measurements of bulk cell population that have frustrated efforts to resolve cellular states. Single-cell genomics and proteomics enable not only precise characterization of cell state, but also provide a stunningly high-resolution view of transitions between states. These measurements may finally make explicit the metaphor that C.H. Waddington posed nearly 60 years ago to explain cellular plasticity: Cells are residents of a vast "landscape" of possible states, over which they travel during development and in disease. Single-cell technology helps not only locate cells on this landscape, but illuminates the molecular mechanisms that shape the landscape itself. However, single-cell genomics is a field in its infancy, with many experimental and computational advances needed to fully realize its full potential.

Culture‐Free Detection of Crop Pathogens at the Single‐Cell Level by Micro‐Raman Spectroscopy

“…To further ensure the sensitivity to discriminate each pathogen, a standard practice should be developed and the number of species within the same genus, subspecies within the same species and pathovars, as well as the number of isolates per strain should be increased (Figure S3, Supporting Information). Moreover, the development of in situ single‐cell sequencing technology32, 33 can be utilized to further examine and improve the accuracy of Raman microspectroscopy for crop pathogen detection. As the standardizing of the operating procedures (sampling methodologies, spectrum collection, signal processing and analysis) and the expansion of scope and depth of reference spectroscopy database, a great promising is held for the practical utility of micro‐Raman spectroscopy in plant quarantine.…”

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

“…However, the analysis of great quantities of data generated at the single cell level faces many challenges. Single‐cell measurements have a significant inherent noise 115, 116, as well as experimental drawbacks associated to batch effects and technical variability 117, 118, 119, and the computational methodologies for accounting for these statistical analysis limitations are still under development. Moreover, the reconstruction of GRNs from single‐cell data performed in several studies has yielded rather small networks, involving only a limited group of regulators 12, 99, 120.…”

“…Moreover, the reconstruction of GRNs from single‐cell data performed in several studies has yielded rather small networks, involving only a limited group of regulators 12, 99, 120. Despite the above‐mentioned limitations and the need for improvements in the statistical analysis of single‐cell data 118, 119, the generation of more comprehensive and accurate measurements of heterogeneity in the pluripotent state will allow the generation of integrative network approaches, such as the one proposed here. These approaches will be helpful for generating models for differentiation of PSCs into specific cell types, pinpointing the signaling pathways, transcriptional factors, epigenetic regulators, and the cross‐talk among them, that could be targeted for improving the efficiency of cell‐type specific differentiation protocols.…”

SDEAP: a splice graph based differential transcript expression analysis tool for population data

“…Each of the six subtypes has different sensitivities to targeted therapies. To understand the functions and mechanisms during cell 20 development or differentiation, differentially expressed transcripts are used to characterize cell types or specificity in a mixed population (Brennecke et al, 2013;Buettner et al, 2015;Trapnell, 2015). Due to the emergent demand for computational tools for DTE and DS analyses without known biological conditions, several methods 25 have been proposed recently.…”

“…Finding DTE genes that could be used as transcriptomic biomarkers to identify subtypes of such diseases could be important for the design of clinical trials to investigate targeted treatments. Moreover, the expression patterns of transcripts in individual cells of different cell types or cell-cycle phases, which can be revealed by the SC RNA-Seq tech-5 nology nowadays, are fundamental to studies on alternative cellular functions during the development of a tissue or an organ (Sasagawa et al, 2013;Trapnell, 2015). Our real data experiments demonstrate that the classification of RNA-Seq samples using the ASEs from SDEAP is much more consistent with the real biological condi-10 tions (i.e.…”