Preimplantation genetic testing for aneuploidies (PGT-A) is increasingly used in assisted reproductive technology to complement morphology-based evaluations by enabling screening for chromosomal abnormalities that would otherwise go undetected. Although PGT-A has been demonstrated to increase the likelihood of a successful pregnancy, particularly when maternal age exceeds 35, the success rate for assisted reproduction with current PGT-A methods has plateaued at 50% (Munné et al, 2019).

Current preimplantation genetic testing (PGT) methods rely heavily on genomic DNA analysis from trophectoderm (TE) biopsies, while RNA is typically discarded during sample preparation. However, integrating transcriptome analysis into PGT workflows offers a deeper understanding of embryo viability and enables the discovery of RNA-based biomarkers that could enhance assisted reproductive technology (ART) outcomes. A recent retrospective study found that certain transcriptional profiles were associated with significantly higher implantation rates (88.6% vs. 50%), underscoring the potential value of incorporating RNA analysis into preimplantation testing (Jin et al, 2024).

To support continued progress in this area, we developed the Embgenix GT-omics Kit—a multiomic solution designed to enable simultaneous genomic and transcriptomic analysis from a single TE biopsy. Based on the same chemistry approaches recommended in Macaulay et al, the workflow combines the proven reproducibility of PicoPLEX whole-genome amplification (WGA) with the high sensitivity of SMART cDNA synthesis and an optimized NGS library preparation method. Accompanying bioinformatics tools provide a seamless end-to-end solution for simultaneous CNV and transcriptome analysis from a single TE biopsy sample.

In this tech note, we show that the Embgenix GT-omics Kit successfully enables simultaneous genome and transcriptome profiling from a single biopsy sample, allowing researchers to obtain more biological insights. DNA-seq and RNA-seq libraries produced using the kit perform comparably to standalone NGS-based PGT-A and transcriptome analysis methods for TE biopsy samples, demonstrating that combining RNA-seq and DNA-seq library production into a single workflow does not affect data quality. Furthermore, we demonstrate the accuracy of transcriptomic data produced with the Embgenix GT-omics Kit through low-input spike-in control experiments.   

Reliable CNV calling and high-quality transcriptomics from TE biopsy samples

In developing a combined DNA-seq and RNA-seq library prep method for TE biopsy samples, it was important that both types of libraries were as robust as those obtained from standalone methods. TE biopsy samples from four embryos (two separate TE biopsies per embryo) were processed using the Embgenix GT-omics Kit. DNA-seq libraries were sequenced, downsampled to 1.5 x 10⁶ paired-end reads, and resulting data was subjected to CNV analysis using Embgenix Analysis Software (Figure 2). Embgenix Analysis Software uses the informative reads and DLRS metrics to assess the suitability of sequencing data for CNV analysis, and all TE biopsy samples satisfied the QC criteria applied by the software. Assay noise metrics (DLRS, MAPD, NME) were similar to those obtained from the standalone Embgenix PGT-A assay (Figure 2, Panel B).

We also compared CNV analysis results to those obtained from an alternative PGT-A assay performed by an outside laboratory. The CNV plots, sample calls, and karyotypes were concordant with the Embgenix GT-omics Kit and the alternative PGT-A assay. The results demonstrate that DNA-seq data generated from TE biopsy samples using the Embgenix GT-omics Kit provides reliable, accurate CNV analysis with minimal background noise comparable to standalone PGT-A methods.

RNA-seq libraries were sequenced, downsampled to 4.0 x 10⁶ paired-end reads per sample, and analyzed using the Cogent NGS Analysis Pipeline (CogentAP). The distribution of reads mapping to exonic, intronic, intergenic, mitochondrial, and ribosomal RNA regions was consistent across each biopsy sample per embryo, and each biopsy sample yielded over 16,000 unique detected genes, with an average of 17,527 genes identified across all six biopsies (Figure 3, Panel A). All RNA-seq libraries provided full gene-body coverage comparable to SMART chemistry (Figure 3, Panel B). A 3′ bias was observed in a single sample—Embryo 4-2—likely reflecting partial RNA degradation introduced during sample preparation, which resulted in reduced 5′ coverage (Figure 3, Panel B).

Accurate transcript quantitation for low-input samples

We have demonstrated the comparable sensitivity of the Embgenix GT-omics method compared to standalone assays using TE biopsy samples. Next, the accuracy and reproducibility of the data was evaluated using synthetic RNA reference standards from the External RNA Controls Consortium (ERCC). These standards consisted of 92 distinct polyadenylated RNA species with known sequences, each present at defined concentrations in one of two formulations (Mix1 vs. Mix2). To simulate real-world TE biopsy sample processing conditions, ERCC standards were combined with five-cell samples derived from the GM05067 cell line at two different dilution levels (low vs. high), generating four distinct RNA concentration conditions. Samples were processed in triplicate using the Embgenix GT-omics Kit and resulting RNA-seq libraries were sequenced, downsampled to 4 x 10⁶ reads per sample, and analyzed using a custom analysis pipeline. Measured RNA quantities were compared with expected values to assess the correlation across the four concentration levels, validating the accuracy and reproducibility of the transcriptomic data obtained.

The resulting profiles for synthetic ERCC transcripts exhibited strong linear correlations (R > 0.99 for intra-mix comparisons and R > 0.93 for comparisons with expected ERCC values), as visualized in a Pearson correlation matrix (Figure 4, Panel A), highlighting the high reproducibility provided by the GT-omics assay. Comparison of measured vs. expected ERCC transcript counts at each of the four concentrations (Figure 4, Panel B) demonstrated the assay’s ability to detect transcripts at an abundance of 100 copies or more with a sequencing depth of 8 x 10⁶ reads. While measured fold changes did not precisely match expected values, particularly at lower abundance levels, a clear correlation was observed between expected and measured fold changes. These results highlight the Embgenix GT-omics Kit’s ability to generate reliable data for differential expression analysis, including quantification of low-abundance transcripts.

Using TE biopsy samples and synthetic RNA reference standards as benchmarks, we demonstrate that the Embgenix GT-omics Kit enables simultaneous genome and transcriptome profiling with sensitivity, accuracy, and reproducibility comparable to standalone methods. This is accomplished through a simple, streamlined workflow compatible with standard molecular biology equipment.

Incorporation of both genomic and transcriptomic profiling during preimplantation testing will allow us to uncover critical details about gene expression patterns, regulatory mechanisms, and cellular processes that influence embryo development and enable the identification of prospective biomarkers for embryo status and implantation potential. These biomarkers may further guide researchers in assessing embryo quality and developmental potential for ART, advancing the field of reproductive medicine, and increasing the chances of live birth delivery.

Samples

TE biopsy samples, collected in collaboration with an external laboratory, were obtained as re-biopsies from embryos that had undergone prior vitrification. To assess the transcriptomic accuracy of the method, ERCC ExFold RNA Spike-In Mixes (ThermoFisher Scientific; Cat. # 4456739) containing blends of 92 different synthetic RNA species in two different formulations were added to the GM05067 lymphoblastoid cell line (Coriell Institute) samples at two different concentrations.

Embgenix GT-omics assay

Lysates were processed according to the Embgenix GT-omics Kit protocol shown in Figure 1. cDNA and WGA products were subjected to enzymatic fragmentation, adapter ligation, and PCR amplification to yield RNA-seq and DNA-seq libraries, respectively. Following clean-up, RNA-seq and DNA-seq libraries were pooled and sequenced. Then, the data was analyzed to identify genomic variants and perform differential expression analysis.

Sequencing

All libraries were sequenced on an Illumina NextSeq 550 using 2 x 75 bp paired-end reads with a NextSeq 500/550 Mid Output v2.5 Kit (150 Cycles; Cat. # 20024904).

Analysis

Following library preparation and sequencing, DNA-seq data were downsampled to 1.5 x 10⁶ paired-end reads per cell, and copy number analysis was performed using Embgenix Analysis software. RNA-seq data were downsampled to 4.0 x 10⁶ paired-end reads per sample and analyzed using CogentAP. For analysis of sequencing data from the ERCC spike-in mixes, RNA-seq data was downsampled to 8 x 10⁶ reads, Illumina adapter sequences were trimmed using Trimmomatic, and reads were aligned with Bowtie2. Mapped read counts were then used to calculate fold changes between samples using a custom analysis pipeline.

Jin, J. et al. Multi-omics PGT: re-evaluation of euploid blastocysts for implantation potential based on RNA sequencing. Hum Reprod. 39, 2861–2872 (2024).

Macaulay, I. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nature methods. 12, 519–522 (2015). 

Munné, S. et al. Preimplantation genetic testing for aneuploidy versus morphology as selection criteria for single frozen-thawed embryo transfer in good-prognosis patients: a multicenter randomized clinical trial. Fertil Steril. 112, 1071–1079 (2019).