• June 25, 2021

A new generation of Ca2+ indicators with greatly improved fluorescence properties.

A new generation of Ca2+ indicators with greatly improved fluorescence properties.

A new family of highly fluorescent indicators has been synthesized for biochemical studies of the physiological role of cytosolic free Ca2+. The compounds combine an 8-coordinate tetracarboxylate chelating site with stilbene chromophores. Incorporation of the ethylenic linkage of the stilbene into a heterocyclic ring enhances the quantum efficiency and photochemical stability of the fluorophore.
Compared to their widely used predecessor, “quin2”, the new dyes offer up to 30-fold brighter fluorescence, major changes in wavelength not just intensity upon Ca2+ binding, slightly lower affinities for Ca2+, slightly longer wavelengths of excitation, and considerably improved selectivity for Ca2+ over other divalent cations. These properties, particularly the wavelength sensitivity to Ca2+, should make these dyes the preferred fluorescent indicators for many intracellular applications, especially in single cells, adherent cell layers, or bulk tissues.

TopHat: discovering splice junctions with RNA-Seq.

A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites.
We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20,000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development.

Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.

High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species.
Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.

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