Multimodal Single-Cell Analysis Reveals Physiological Maturation in the Developing Human Neocortex.

In the developing human neocortex, progenitor cells generate diverse cell types prenatally. Progenitor cells and newborn neurons respond to signaling cues, including neurotransmitters. While single-cell RNA sequencing has revealed cellular diversity, physiological heterogeneity has yet to be mapped onto these developing and diverse cell types. By combining measurements of intracellular Ca2+ elevations in response to neurotransmitter receptor agonists and RNA sequencing of the same single cells, we show that Ca2+ responses are cell-type-specific and change dynamically with lineage progression. Physiological response properties predict molecular cell identity and additionally reveal diversity not captured by single-cell transcriptomics. We find that the serotonin receptor HTR2A selectively activates radial glia cells in the developing human, but not mouse, neocortex, and inhibiting HTR2A receptors in human radial glia disrupts the radial glial scaffold. We show highly specific neurotransmitter signaling during neurogenesis in the developing human neocortex and highlight evolutionarily divergent mechanisms of physiological signaling.

Corrigendum: Fluidic Logic Used in a Systems Approach to Enable Integrated Single-Cell Functional Analysis.

[This corrects the article on p. 70 in vol. 4, PMID: 27709111.].

Fluidic Logic Used in a Systems Approach to Enable Integrated Single-Cell Functional Analysis.

The study of single cells has evolved over the past several years to include expression and genomic analysis of an increasing number of single cells. Several studies have demonstrated wide spread variation and heterogeneity within cell populations of similar phenotype. While the characterization of these populations will likely set the foundation for our understanding of genomic- and expression-based diversity, it will not be able to link the functional differences of a single cell to its underlying genomic structure and activity. Currently, it is difficult to perturb single cells in a controlled environment, monitor and measure the response due to perturbation, and link these response measurements to downstream genomic and transcriptomic analysis. In order to address this challenge, we developed a platform to integrate and miniaturize many of the experimental steps required to study single-cell function. The heart of this platform is an elastomer-based integrated fluidic circuit that uses fluidic logic to select and sequester specific single cells based on a phenotypic trait for downstream experimentation. Experiments with sequestered cells that have been performed include on-chip culture, exposure to various stimulants, and post-exposure image-based response analysis, followed by preparation of the mRNA transcriptome for massively parallel sequencing analysis. The flexible system embodies experimental design and execution that enable routine functional studies of single cells.

Characterizing the composition of molecular motors on moving axonal cargo using "cargo mapping" analysis.

Understanding the mechanisms by which molecular motors coordinate their activities to transport vesicular cargoes within neurons requires the quantitative analysis of motor/cargo associations at the single vesicle level. The goal of this protocol is to use quantitative fluorescence microscopy to correlate ("map") the position and directionality of movement of live cargo to the composition and relative amounts of motors associated with the same cargo. "Cargo mapping" consists of live imaging of fluorescently labeled cargoes moving in axons cultured on microfluidic devices, followed by chemical fixation during recording of live movement, and subsequent immunofluorescence (IF) staining of the exact same axonal regions with antibodies against motors. Colocalization between cargoes and their associated motors is assessed by assigning sub-pixel position coordinates to motor and cargo channels, by fitting Gaussian functions to the diffraction-limited point spread functions representing individual fluorescent point sources. Fixed cargo and motor images are subsequently superimposed to plots of cargo movement, to "map" them to their tracked trajectories. The strength of this protocol is the combination of live and IF data to record both the transport of vesicular cargoes in live cells and to determine the motors associated to these exact same vesicles. This technique overcomes previous challenges that use biochemical methods to determine the average motor composition of purified heterogeneous bulk vesicle populations, as these methods do not reveal compositions on single moving cargoes. Furthermore, this protocol can be adapted for the analysis of other transport and/or trafficking pathways in other cell types to correlate the movement of individual intracellular structures with their protein composition. Limitations of this protocol are the relatively low throughput due to low transfection efficiencies of cultured primary neurons and a limited field of view available for high-resolution imaging. Future applications could include methods to increase the number of neurons expressing fluorescently labeled cargoes.

Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex.

Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships but require efficient methods for cell capture and mRNA sequencing. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths, the limitations of shallow sequencing have not been investigated directly. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In the developing cortex, we identify diverse cell types, including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.

Endogenous GSK-3/shaggy regulates bidirectional axonal transport of the amyloid precursor protein.

Neurons rely on microtubule (MT) motor proteins such as kinesin-1 and dynein to transport essential cargos between the cell body and axon terminus. Defective axonal transport causes abnormal axonal cargo accumulations and is connected to neurodegenerative diseases, including Alzheimer's disease (AD). Glycogen synthase kinase 3 (GSK-3) has been proposed to be a central player in AD and to regulate axonal transport by the MT motor protein kinesin-1. Using genetic, biochemical and biophysical approaches in Drosophila melanogaster, we find that endogenous GSK-3 is a required negative regulator of both kinesin-1-mediated and dynein-mediated axonal transport of the amyloid precursor protein (APP), a key contributor to AD pathology. GSK-3 also regulates transport of an unrelated cargo, embryonic lipid droplets. By measuring the forces motors generate in vivo, we find that GSK-3 regulates transport by altering the activity of kinesin-1 motors but not their binding to the cargo. These findings reveal a new relationship between GSK-3 and APP, and demonstrate that endogenous GSK-3 is an essential in vivo regulator of bidirectional APP transport in axons and lipid droplets in embryos. Furthermore, they point to a new regulatory mechanism in which GSK-3 controls the number of active motors that are moving a cargo.

Subpixel colocalization reveals amyloid precursor protein-dependent kinesin-1 and dynein association with axonal vesicles.

Intracellular transport of vesicles and organelles along microtubules is powered by kinesin and cytoplasmic dynein molecular motors. Both motors can attach to the same cargo and thus must be coordinated to ensure proper distribution of intracellular materials. Although a number of hypotheses have been proposed to explain how these motors are coordinated, considerable uncertainty remains, in part because of the absence of methods for assessing motor subunit composition on individual vesicular cargos. We developed a robust quantitative immunofluorescence method based on subpixel colocalization to elucidate relative kinesin-1 and cytoplasmic dynein motor subunit composition of individual, endogenous amyloid precursor protein (APP) vesicles in mouse hippocampal cells. The resulting method and data allow us to test a key in vivo prediction of the hypothesis that APP can recruit kinesin-1 to APP vesicles in neuronal axons. We found that APP levels are well-correlated with the amount of the light chain of kinesin-1 (KLC1) and the heavy chain of cytoplasmic dynein (DHC1) on vesicles. In addition, genetic reduction of APP diminishes KLC1 and DHC1 levels on APP cargos. Finally, our data reveal that reduction of KLC1 leads to decreased levels of DHC1 on APP vesicles, suggesting that KLC1 is necessary for the association of DHC1 to these cargos, and help to explain previously reported retrograde transport defects generated when kinesin-1 is reduced.

Molecular motor function in axonal transport in vivo probed by genetic and computational analysis in Drosophila.

Bidirectional axonal transport driven by kinesin and dynein along microtubules is critical to neuronal viability and function. To evaluate axonal transport mechanisms, we developed a high-resolution imaging system to track the movement of amyloid precursor protein (APP) vesicles in Drosophila segmental nerve axons. Computational analyses of a large number of moving vesicles in defined genetic backgrounds with partial reduction or overexpression of motor proteins enabled us to test with high precision existing and new models of motor activity and coordination in vivo. We discovered several previously unknown features of vesicle movement, including a surprising dependence of anterograde APP vesicle movement velocity on the amount of kinesin-1. This finding is largely incompatible with the biophysical properties of kinesin-1 derived from in vitro analyses. Our data also suggest kinesin-1 and cytoplasmic dynein motors assemble in stable mixtures on APP vesicles and their direction and velocity are controlled at least in part by dynein intermediate chain.

Stable kinesin and dynein assemblies drive the axonal transport of mammalian prion protein vesicles.

Kinesin and dynein are opposite-polarity microtubule motors that drive the tightly regulated transport of a variety of cargoes. Both motors can bind to cargo, but their overall composition on axonal vesicles and whether this composition directly modulates transport activity are unknown. Here we characterize the intracellular transport and steady-state motor subunit composition of mammalian prion protein (PrP(C)) vesicles. We identify Kinesin-1 and cytoplasmic dynein as major PrP(C) vesicle motor complexes and show that their activities are tightly coupled. Regulation of normal retrograde transport by Kinesin-1 is independent of dynein-vesicle attachment and requires the vesicle association of a complete Kinesin-1 heavy and light chain holoenzyme. Furthermore, motor subunits remain stably associated with stationary as well as with moving vesicles. Our data suggest a coordination model wherein PrP(C) vesicles maintain a stable population of associated motors whose activity is modulated by regulatory factors instead of by structural changes to motor-cargo associations.

Identifying statistical dependence in genomic sequences via mutual information estimates.

Questions of understanding and quantifying the representation and amount of information in organisms have become a central part of biological research, as they potentially hold the key to fundamental advances. In this paper, we demonstrate the use of information-theoretic tools for the task of identifying segments of biomolecules (DNA or RNA) that are statistically correlated. We develop a precise and reliable methodology, based on the notion of mutual information, for finding and extracting statistical as well as structural dependencies. A simple threshold function is defined, and its use in quantifying the level of significance of dependencies between biological segments is explored. These tools are used in two specific applications. First, they are used for the identification of correlations between different parts of the maize zmSRp32 gene. There, we find significant dependencies between the 5' untranslated region in zmSRp32 and its alternatively spliced exons. This observation may indicate the presence of as-yet unknown alternative splicing mechanisms or structural scaffolds. Second, using data from the FBI's combined DNA index system (CODIS), we demonstrate that our approach is particularly well suited for the problem of discovering short tandem repeats-an application of importance in genetic profiling.

An optimal DNA segmentation based on the MDL principle.

The biological world is highly stochastic and inhomogeneous in its behaviour. There are regions in DNA with a high concentration of G or C bases; stretches of sequences with an abundance of CG dinucleotide (CpG islands); coding regions with strong periodicity-of-three pattern, and so forth. Transitions between these regions of DNA, known also as change points, carry important biological information. Computational methods used to identify these homogeneous regions are called segmentations. Viewing a DNA sequence as a non-stationary process, we apply recent novel techniques of universal source coding to discover stationary (possibly recurrent) segments. In particular, the Stein-Ziv lemma is adopted to find an asymptotically optimal discriminant function that determines whether two DNA segments are generated by the same source assuring exponentially small false positives. Next, we use the Minimum Description Length (MDL) principle to select parameters that lead to a linear-time segmentation algorithm. We apply our algorithm to human chromosome 9 and chromosome 20 to discover coding and noncoding regions, starting positions of genes, as well as the beginning of CpG islands.