Advances in genomics have made it easier than ever to study individual cells and understand how they contribute to complex biological systems. Researchers at BioSpyder Technologies have developed a new technology called TempO-LINC, a cutting-edge platform that allows scientists to perform high-throughput transcriptomic analysis on single cells and even cell nuclei. This new method offers a range of benefits for researchers looking to analyze gene expression without the need for complicated steps like cDNA generation.

What is TempO-LINC?

TempO-LINC is a genomics platform that allows researchers to analyze the gene expression profiles of individual cells without first generating complementary DNA (cDNA). Instead, the system uses molecular barcodes—tiny labels attached to gene expression probes that identify each individual cell. These barcodes allow researchers to track and analyze gene expression from a diverse range of cell types, even in large numbers. By adding a barcode to each probe within a fixed cell, TempO-LINC can reconstruct the gene expression profile of a single cell, whether you’re analyzing hundreds of cells or more than 100,000 in a single experiment.

How Does TempO-LINC Work?

TempO-LINC uses a process called combinatorial indexing, which tags all probes within the same cell with an identical barcode. This approach enables the reconstruction of high-resolution gene expression profiles from a large number of cells. The technology is highly scalable, and in the study reported, TempO-LINC used more than 5.3 million unique barcodes, providing precise and accurate measurements of gene expression across a vast array of cells and tissues.

TempO-LINC workflow and study overview

(a) The TempO-LINC workflow begins with fixation of cells or nuclei and subsequent storage at −80^oC. Fixed cells are thawed and hybridized with Detector Oligo (DO) probe pools that are then ligated. Split pool barcoding with three rounds of ligation is then performed. Following ligation of barcodes, cells are pooled again and redistributed into a final 96-well plate where sequencing adaptors and index sequences are added during PCR amplification. Amplified libraries are then purified and sequenced. Data analysis begins with demultiplexing to associate gene expression profiles with cell-identifying barcodes. (b) Schematic of TempO-LINC 310 bp library structure including the three barcodes that are ligated as well as the i5 and i7 index positions that serve as a fourth barcode to identify cells. The table shows total barcode diversity created through each round of barcoding as well as the molecular mechanism for barcoding. (c) Overview of TempO-LINC validation experiments performed for this study. Experiments were a combination of gene expression profiling on mixtures of multiple human and mouse cell lines as well as nuclei isolated from adult mouse brain, lung and kidney tissue.

Advantages of TempO-LINC

One of the standout features of TempO-LINC is its ability to perform high-sensitivity gene detection with minimal sample preparation. It works with a wide range of sample types, making it versatile for a variety of biological research projects. The assay also boasts a low multiplet rate (less than 1.1%) and a cell capture rate of approximately 50%, meaning the technology is both accurate and efficient.

TempO-LINC can be used for comprehensive transcriptomic profiling, measuring gene expression across thousands of genes. However, one of its key advantages is that it can be targeted to measure specific genes or molecular pathways of interest. This targeted approach reduces the need for extensive sequencing, saving both time and resources.

Applications in Large-Scale Studies

In the study detailed in the article, TempO-LINC was applied to profile the transcriptomes of over 90,000 cells from a variety of species, including mouse lung, kidney, and brain tissues. This enabled the identification and annotation of more than 50 distinct cell populations, each with its own unique gene expression profile. The data also demonstrated a strong correlation between gene expression and cell type-specific molecular markers, highlighting the platform’s ability to accurately capture the diversity of cell types in complex tissues.