Long-read assembly reveals vast transcriptional complexity in the placenta associated with metabolic and endocrine function

Domain: biology
Published: 2026-04-02
Editorial outcome: minor_revision

Abstract
The placenta is critical for fetal development and mediates effects of pregnancy complications on offspring
metabolic health yet remains poorly characterized in genomic studies. Existing transcriptomic analyses
rely on adult tissue reference annotations, overlooking developmentally important isoform diversity.
Using
largest-in-class long-read RNA-seq (n = 72), we create a comprehensive placental transcriptome reference
identifying 37,661 high-confidence isoforms (14,985 previously unannotated) across 12,302 genes (2,759
previously unannotated). Contrary to characterizations of the placenta as a transcriptomic void, we find
transcriptional breadth and complexity comparable to adult tissues, with high splicing diversity of obesity-
and growth-related gene transcripts, including 108 distinct CSH1 (placental lactogen) isoforms.
Applying
this reference to short-read RNA-seq from diverse populations (n = 352) reduced inferential uncertainty in
isoform quantification by approximately 30%. We find placental transcription mediated 36% of gestational
diabetes mellitus effects on birth weight, with ancestry-specific effects including previously unannotated
CSH1 isoforms mediating larger effects in European (24.4%) than Asian (13.4%) populations. These findings
illustrate the importance of tissue-specific, long-read annotations for isoform-resolved transcriptomics.

Introduction
The placenta, a transient organ unique to pregnancy, serves as the critical interface between maternal and
fetal environments. Beyond its essential roles in nutrient transfer, gas exchange, and waste elimination,
the placenta functions as a master regulator of the intrauterine environment through complex
neuroendocrine and immunological signaling pathways that influence fetal development and metabolic
programming of later-in-life health outcomes.1–9
Gestational diabetes mellitus (GDM), characterized by glucose intolerance with onset during pregnancy,
affects approximately 16% of pregnancies worldwide and significantly impacts both maternal and fetal
health.9 While decreasing insulin sensitivity characterizes physiological pregnancy, GDM involves insulin
insufficiency relative to the exaggerated decline in maternal insulin sensitivity and glucose metabolism,
potentially through altered placenta-mediated endocrine signaling such as the pro-diabetogenic human
placental lactogen (hPL) encoded by CSH1.10 GDM-exposed fetuses experience placenta-mediated
alterations in intrauterine signals promoting macrosomia, altered body composition with increased
adiposity, and heightened risk for metabolic disorders later in life.11,12 These associations are in line with
the Developmental Origins of Health and Disease (DOHaD) hypothesis, which posits that the intrauterine
environment shapes developmental trajectories with lasting implications for offspring health.13–17
Despite the placenta's central role in prenatal development, it has been notably underrepresented in
large-scale tissue-specific genomic and transcriptomic initiatives such as the Genotype-Tissue Expression
Project (GTEx), the NIH Roadmap Epigenomics Consortium, and the Encyclopedia of DNA Elements
Consortium (ENCODE).18–20 These limited resources have restricted our understanding of placenta-specific
gene regulation and transcriptional breadth and complexity relevant to fetal growth and metabolic
programming.
Developing tissues, including the placenta, exhibit distinct patterns of alternative splicing and isoform
usage compared to adult tissues.20–23 Using adult tissue or species-level aggregate transcriptome
references like GENCODE24 for placental transcriptomics obscures tissue-specific regulatory mechanisms
critical to understanding placental function. This limitation is compounded by the inherent constraints of
short-read RNA sequencing, which faces significant challenges in accurately resolving transcript isoforms
due to read-to-transcript mapping ambiguity.25 When sequenced fragments are compatible with the exons
of multiple isoforms, probabilistic read assignment introduces significant uncertainty in estimating
isoform-level expression,26 a problem particularly acute in developing tissues exhibiting extensive
alternative splicing.22
Analyses of existing placental RNA-sequencing datasets have focused overwhelmingly on aggregated
gene-level measurements,5,14,21,23,27–34 missing critical biological information by obscuring isoforms that
may be particularly susceptible to genetic or environmental effects relevant to complex traits.35–41 This
gene-centric view fails to capture scenarios where pathological exposures are mediated through changes
in specific isoforms that could be masked or compensated by expression of other, potentially non-
functional isoforms, or have opposing functions on the same biological process.35 For example, previous
studies that challenged the role of classical reproductive hormones in GDM, based on findings that
circulating hPL levels do not correlate with insulin sensitivity changes during pregnancy,42 measured
aggregate hormone levels without distinguishing between functionally distinct isoforms that may have
differential effects on maternal metabolism.
Long-read RNA sequencing technologies generate individual reads of up to 15-20 kilobases, sufficient to
capture entire transcripts from end to end, providing direct evidence of exon combinations and revealing
comprehensive transcriptional breadth and complexity.43–47 Recent studies demonstrate that over half of
the isoforms detected using long-read RNA sequencing were missed by traditional short-read approaches
in the same tissue samples,48–50 highlighting a significant gap in current understanding of transcriptome
regulation. Previous work, including from our group, demonstrated that alternative splicing patterns and
isoform usage explain substantially more variance in complex phenotypes than gene expression, with
modeling of isoforms rather than genes increasing discovery of transcriptome-mediated genetic
associations with complex traits by approximately 60%.35,37,51
Here, we develop a comprehensive transcriptome reference for placenta derived from n = 72 long-read
RNA-sequencing libraries and orthogonal sequencing data. Our analysis identified 37,661 high-confidence
transcripts representing 12,302 genes, including isoforms of both known and potentially novel genes, with
pregnancy-related genes exhibiting extraordinary isoform diversity compared to other human tissues. We
show that this assembly offers two complementary advantages: (1) improved specificity and accuracy of
transcript quantification in short-read datasets (n = 352) by reducing mapping ambiguity and (2) the ability
to identify isoform-level regulatory mechanisms that may be critical for understanding GDM-associated
modulation of fetal development. We leveraged this annotation to study GDM and fetal growth,
demonstrating how improved isoform resolution reveals molecular mechanisms underlying complex
pregnancy outcomes. Our approach uncovered previously uncharacterized placental isoforms of genes
involved in glucose metabolism, placental lactogen production and fetal growth regulation that mediate
the effects of maternal hyperglycemia on birth weight. These findings demonstrate that long-read
sequencing-derived placental transcript annotations are essential for understanding pregnancy
pathophysiology and detecting mechanistic signals in observational studies of environmental and
intrauterine exposures. Given the improved quantification precision and novel mechanistic insights
demonstrated here, our placenta reference transcriptome provides a valuable resource for reanalyzing
existing short-read datasets to uncover previously missed isoform-level regulatory mechanisms.

Results
Long-read sequencing reveals placental transcriptional diversity
Using samples collected from a subset of two prospective prebirth cohorts,28,52 we generated long-read
transcriptome data from villous placental tissue of n = 72 term, live births without known placentation
pathologies, such as intrauterine growth restriction and pre-eclampsia, including 50% from pregnancies
affected by GDM (Figure 1A). Using the Oxford Nanopore platform, we sequenced cDNA libraries obtaining
approximately 310.8 million reads. After stringent quality control and alignment to hg38 (Methods), we
retained approximately 72 million high-quality reads with an average read length of 1.07 kilobases (kb)
(SD = 1.25 kb, range = 0.25–22.2 kb) (Supplementary Data 1). These reads mapped to 68,002 distinct
transcripts with a mean length of 1.36 kb (SD = 1.67 kb, range = 0.06–205 kb) (Supplementary Figure 1A)
and an average of 4.5 exons per transcript (Supplementary Figure 1B). These transcripts represented
23,069 genes, with an average of 2.95 isoforms per gene (SD = 3.84) (Supplementary Figure 1C). Our
analysis captured 20,803 of 63,187 genes annotated in GENCODE v45 and identified 2,266 previously
unannotated genes including 102 fusion products of multiple genes (Supplementary Data 2).
One-quarter of placental transcripts are unannotated
Among transcripts annotated to known genes (n = 65,386 [96.15%]) we identified three major categories
by comparison to GENCODE v45: transcripts matching known annotations, previously unannotated
isoforms of known genes, and transcripts of previously unannotated genes (Figure 1B-C). Most transcripts
of known genes matched existing annotations, classified as full splice match (FSM: n = 45,082 [68.95%]),
where all splice junctions exactly matched a reference transcript, or incomplete splice match (ISM: n =
5,176 [7.91%]), where a subset of splice junctions matched a reference.
We identified 14,957 previously unannotated transcripts (22.87% of transcripts expressed in placenta)
associated with 5,630 known genes (26.93% of genes expressed in placenta). These previously
unannotated isoforms averaged 0.79 kb in length (SD = 0.47 kb, range = 0.08–4.4 kb) with mean of 4.5
exons per transcript. The previously unannotated transcripts are categorized as: “novel not in catalog”
(NNC, n = 9,578 [63.9%]), with at least one previously unannotated splice donor or acceptor site; “novel
in catalog” (NIC, n = 5,190, [34.6%]), combining known splice sites in previously unobserved patterns; or
“genic” transcripts (n = 217 [1.45%]), which partially mapped to 152 known genes but with insufficient
splice junction matches to be classified as NIC or NNC. Additionally, we identified 2,759 transcripts
representing previously unannotated genes, categorized as: antisense (n = 1,421, [51.5%]), intergenic (n =
1,006 [36.5%]), originating within introns of known genes (n = 189 [6.8%]), or fusion products spanning
multiple genes (n = 143 [5.2%]).
Orthogonal data validate high-confidence transcript models
For downstream analyses, we filtered out low-confidence isoforms using criteria from previous long-read
transcriptome assembly studies (Methods).36,45–49 We used orthogonal short-read RNA-seq, DNase-seq,
CAGE-seq, and SAPAS data to validate splice junction coverage and transcription start and termination sites
(TSS and TTS) (Supplementary Data 16). Transcript models showed robust support (Table 1; Supplementary
Data 2) with an average of 554.23 long-reads (Nanopore; SD = 3.89 × 104) (Supplementary Figure 2A) and
an average minimum splice junction support of 2.33 × 104 short-reads (Illumina; SD = 2.47 × 105)
(Supplementary Figure 2B). We observed strong enrichment of TSS near CAGE-seq or DNase-seq peaks
(median distance = 90 bp; 23,740 [34.9%] of transcripts within 50 bp) (Supplementary Figure 2C), and TTS
near alternative polyadenylation sites or PolyA motifs (median distance = 15 bp; 26,549 [40.5%] of
transcripts within 5 bp) (Supplementary Figure 2D–E).
Quality filtering identified few problematic transcripts: n = 2,243 isoforms (3.3%) showed evidence of
nonsense-mediated decay (NMD), n = 483 (0.7%) exhibited RT-switching artifacts, and n = 4 (0.005%)
showed evidence of PolyA intrapriming (Supplementary Figure 2F). While NMD transcripts can be
biologically meaningful, incomplete assemblies or artifacts can also produce premature termination
codons. Therefore, for NMD-predicted transcripts, we required additional evidence of substantial 5'
expression to distinguish genuine NMD substrates from assembly artifacts; of 2,243 NMD-predicted
transcripts, 892 were filtered exclusively due to insufficient 5' expression evidence and the remaining
1,351 were filtered for other quality control reasons. After removing 30,341 low-confidence isoforms
(44.62%; Supplementary Data 3), our placenta transcript assembly contained 37,661 high-confidence
isoforms (Figure 1C) with mean length of 1.60 kb (SD = 1.52 kb, range = 0.08–34.63 kb; Supplementary
Figure 3A) and an average of 5.62 exons per transcript (Supplementary Figure 3B), representing 12,302
genes (mean = 3.06, SD = 3.43 isoforms per gene), including 11,854 GENCODE v45 genes and 448
previously unannotated genes (Supplementary Figure 3C). Among the 500 most abundant placental long-
read transcripts, we observed a 4.3-fold enrichment for placenta-specific genes (adjusted p = 7.85 × 10-7;
Supplementary Data 4; Supplementary Figure 3D).
Unannotated transcripts show distinct structural features
Compared to transcripts of known genes, transcripts of previously unannotated genes were significantly
less abundant in the long-reads (Mann-Whitney-Wilcoxon test: W = 1.07 × 107, p < 2.2 × 10-16)
(Supplementary Figure 4A), shorter in length (W = 1.31 × 107, p < 2.2 × 10-16) (Supplementary Figure 4B),
and contained fewer exons (W = 1.43 × 107, p < 2.2 × 10-16) (Supplementary Figure 4C). Similarly, previously
unannotated transcripts of annotated genes were significantly less abundant than FSM transcripts (W =
1.25 × 108, p < 2.2 × 10-16) and shorter in length (W = 1.58 × 108, p < 2.2 × 10-16), although they contained
comparable numbers of exons (W = 1.15 × 108, p = 0.99). We identified substantial isoform diversity in
multi-exonic RNA isoforms (range: 1–108 isoforms per gene) with over half of all detected genes (n = 7,343
[59.69%]) expressing more than one isoform (Figure 2A). The number of detected isoforms was correlated
with the number of detected exons per isoform (r = 0.87, p < 2.2 × 10-16) and the strength of this correlation
was stronger among highly expressed genes (r = 0.93, p < 2.2 × 10-16), likely reflecting greater sensitivity
for detecting splicing events from abundantly expressed genes (Supplementary Figure 4D).
These structural features are consistent with patterns observed in evolutionarily younger genes across
multiple species, where gene length and transcript complexity show directional trends toward increasing
structural elaboration over evolutionary time.54 Approximately half of the previously unannotated
transcripts in our assembly are non-coding, raising the possibility that some may represent transcriptional
noise rather than functional innovations under selection. However, definitive characterization of
evolutionary origin and functional significance would require synteny analysis, phylogenetic
reconstruction, or experimental validation to distinguish between lineage-specific functional genes,
neutrally-evolving transcripts, and transcriptional noise.55–60
The placenta is not a transcriptomic void
To evaluate placental transcriptional breadth and complexity with reference to other human tissues, we
reprocessed 15 GTEx47 tissues and cell lines using an identical bioinformatic pipeline to our placental
samples (Tables S16–S17; Methods). The placenta exhibits transcriptional breadth (total number of
isoforms and genes expressed) comparable to other human tissues (Figure 1D), with saturation analysis
confirming sufficient sequencing depth for robust comparison (Figure 1E). Transcriptional complexity (the
mean number of isoforms per gene) ranged from 1.8 to 2.8 across tissues (Figure 1F). These findings
challenge previous characterizations of the placenta as a transcriptomic void expressing few, high-
abundance transcripts.21,23,61 Instead, the placenta exhibits transcriptional breadth and complexity
comparable to other tissues and utilizes complex splicing regulation—particularly for genes involved in
maternal-fetal signaling.
Pregnancy-related genes show high transcriptional complexity
While transcriptional breadth and complexity of the placenta are comparable to other tissues, it exhibits
relatively isoform diversity in genes with critical functions in pregnancy and placental development.
Chorionic somatomammotropin hormone 1 (CSH1), which regulates human placental lactogen production
in pregnancy,62 displayed the greatest transcriptional diversity with 108 distinct isoforms (Figure 1F,
maximum values shown in brackets; Figure 2A-B). This was followed by pregnancy-specific glycoproteins
PSG5 (56 isoforms) and PSG6 (48 isoforms), and the CSH-like gene CSHL1 (50 isoforms). Similarly, growth
hormone 2 (GH2), which encodes placental growth hormone, also showed expanded transcriptional
diversity with 22 distinct isoforms. Both CSH1 and GH2 belong to the growth hormone/chorionic
somatomammotropin gene family clustered on chromosome 17, and their protein products play
coordinated roles in maternal metabolic adaptation to pregnancy and fetal growth regulation. The
extensive transcriptional diversity across this gene family suggests complex regulation of placental
endocrine functions, supported by significant enrichment for hormone receptor binding (odds ratio = 79.3,
adjusted p = 1.29 × 10-3) for the 50 most isoform-rich genes through Gene Ontology (GO) analysis
(Supplementary Data 5).
Unannotated transcripts encode potentially functional proteins
We evaluated the coding potential and peptide validation of detected transcripts. Open reading frames
(ORFs) were predicted for 19,670 (52.2%) isoforms (Supplementary Figure 5A), with similar coding
sequencing (CDS) length across structural categories (median = 1.11 kb) (Supplementary Figure 5B).
Coding potential, measured by CPC2 coding probability scores,63 varied by structural category: FSM, NIC,
and NNC transcripts showed similarly high coding potential (median = 85.39%) while ISM (median =
66.02%) and other categories (median = 57.19%) displayed lower potential (Supplementary Figure 5C).
We used BlastP64 to identify isoform-unique peptides from tandem mass spectrometry (MS/MS) of human
placental tissue, including GDM-affected pregnancies,65,66 ensuring validation of isoform-specific features.
We found that 15.2% of reference transcripts (FSM) and 5.0-5.2% of novel isoforms (NIC, NNC) contained
unique peptides detected in placental MS/MS (Figure 2C; Supplementary Data 6). Notably, ISM (22.7%)
and genic genomic transcripts (20.0%) showed higher validation rates in placental MS/MS compared to
the full UniProt database (9.1% and 10.0%, respectively). In contrast, the full UniProt database revealed
higher validation rates for reference and novel junction transcripts (FSM: 31.1%, NIC: 15.0%, NNC: 13.6%),
with lower validation in the tissue-specific MS/MS reflecting the limited dynamic range and condition-
specific nature of proteomic detection.
We identified 80 transcripts of novel genes (17.62%) with predicted ORFs—44 of which show MS/MS
validation—encoding functionally relevant proteins including fibronectin (a potential GDM biomarker),67
E-cadherin (regulating trophoblastic differentiation),68 alpha-enolase (5 distinct isoforms, differentially
expressed in GDM),69 occludin (placental barrier integrity),70 and CCL14 (trophoblast migration and
maternal-fetal communication).71 Transcripts without peptide validation may include both non-coding
RNAs, which can have important regulatory functions, as well as proteins that may be below MS/MS
detection limits due to low abundance, post-translational modifications affecting peptide recovery,
condition-specific expression not represented in available proteomic datasets, or cell-type-specific
expression diluted in bulk tissue samples.
Placenta annotations reduce short-read quantification uncertainty
To evaluate the impact of transcript discovery and filtration on short-read transcript quantification, we
compared quantifications against our placenta reference transcriptome, GENCODE v45, and their union
(GENCODE+). We analyzed Illumina cDNA libraries from villous placental tissue collected in two
prospective prebirth cohorts: GUSTO28 (n = 200; Methods) and the Genetics of Glucose regulation in
Gestation and Growth (Gen3G29; n = 152; Methods), both with approximately 20% of samples from GDM-
affected pregnancies (Figure 3A; Supplementary Data 7; Supplementary Figure 6A).
Our placenta reference transcriptome demonstrated population-specific differences in total quantified
expression that varied by cohort ancestry. In the GUSTO cohort (Singaporean), which shared ancestry with
the samples used to generate the placenta reference transcriptome, lr-assembly achieved the highest total
quantified expression with an increase of 7,351 log(TPM) over GENCODE v45 (17.9% increase, p < 2 × 10-
16) and 4,622 log(TPM) over GENCODE+ (10.6% , p < 2 × 10-16) (Supplementary Data 8). In contrast, the
Gen3G cohort (white European) showed the highest total quantified expression with GENCODE+, with an
increase of 13,044 log(TPM) over GENCODE v45 (31.2%, p < 2 × 10-16). The lr-assembly alone provided a
smaller but significant 3,896 log(TPM) increase over GENCODE v45 in Gen3G (9.3%, p = 1.45 × 10-10). These
differences in total quantified expression across cohorts (interaction p < 2 × 10-16) likely reflect the ancestry
composition of our long-read samples, with GENCODE+ detecting additional isoforms in Gen3G not
captured in the Singaporean-derived lr-assembly (Figure 3B).
Despite these ancestry-specific differences in total quantified expression, transcripts that failed to meet
our stringent quality criteria showed significantly lower mean expression and variance in the short-read
quantifications compared to FSM and previously unannotated transcripts in both cohorts (Figure 3C;
Supplementary Figure 6B). Statistical analysis revealed a clear hierarchy in transcript expression levels and
variance (Supplementary Data 9): shared (FSM) transcripts showed the highest expression, followed by
previously unannotated transcripts, then filtered transcripts, and finally absent transcripts. In GUSTO,
shared transcripts had 1.14–1.67 log-fold higher expression than other categories (all p < 1 × 10-4), with
expression variance showing similar patterns (1.88–2.78 log-fold differences, all p < 1 × 10-4). Importantly,
previously unannotated transcripts showed expression levels only slightly lower than shared transcripts,
both of which had higher expression levels than filtered or absent transcripts, confirming that our
assembly captures functionally relevant isoforms rather than low-quality artifacts. Notably, transcripts
absent from our lr-assembly but present in GENCODE (which contributed to Gen3G's higher expression
with GENCODE+) also showed significantly lower expression and variance, suggesting many represent low-
confidence annotations.
To formally quantify read-to-transcript mapping ambiguity, we calculated transcript-level inferential
relative variance (InfRV) for each sample across both cohorts. As InfRV is roughly stabilized with respect to
mean expression level, higher InfRV values indicate greater uncertainty in estimating transcript
abundance72 (Methods). Mean transcript-level InfRV was consistently reduced when short reads were
quantified against our placenta reference transcriptome compared to both GENCODE v45 (30.4% reduced
in GUSTO and 30.3% in Gen3G) and GENCODE+ (31.5% reduced in GUSTO and 30.4% in Gen3G) (Figure
3D; Supplementary Figure 6C), indicating improved isoform-level quantification precision. Critically, this
improvement was observed in both cohorts despite ancestry-specific differences in total expression,
indicating that the lr-assembly reduces mapping ambiguity even for the Gen3G cohort where GENCODE+
achieved higher total quantified expression. When stratified by structural category, transcripts in "Other"
categories (antisense, intergenic, fusion, and genic genomic) displayed low mean InfRV and the weakest
between-sample correlations (Supplementary Figure 6D–G), suggesting unambiguous read mapping due
to simpler transcript structures but high biological or technical variance reflecting potentially stochastic
transcription or lower abundance that amplifies technical noise.
As reduced InfRV is positively correlated with reduced exon sharing between transcripts (Supplementary
Figure 6H; r = 0.687, p = 3.85 × 10-279), smaller annotations would be expected to show lower InfRV values.
To confirm that our observed reduction reflected tissue-matched isoform content rather than annotation
size alone, we quantified placental short-read RNA-seq against long-read transcript assemblies for two
GTEx tissues: subcutaneous adipose (one of the smallest of our assemblies with 13,829 transcripts) and
cultured fibroblasts (the largest; 47,850 transcripts). Both GTEx annotations reduced InfRV relative to
GENCODE v45 or GENCODE+, consistent with reduced exon sharing from smaller annotations. However,
the placenta reference had the lowest mode InfRV (mode log of InfRV estimated by kernel density:
placenta = -1.05, adipose = -0.86, fibroblasts = -0.86; Supplementary Figure 6I). This demonstrates that
non-tissue-matched assemblies contain tissue-specific isoforms not expressed in the tissue of interest,
introducing irrelevant exon-sharing complexity. In contrast, a tissue-specific annotation process both
excludes irrelevant transcripts through assembly and filtering and includes relevant, previously
unannotated isoforms through discovery. These data confirm that tissue-matched long-read annotations
improve quantification precision beyond what would be expected from annotation size reduction alone
and that the benefits of our placenta reference transcriptome extend across ancestry groups, even when
not all ancestry-specific isoforms are captured.
Placenta annotations reveal extensive GDM-associated changes
We next examined differences at gene and transcript levels associated with GDM status in GUSTO and
Gen3G. In the following analyses, we excluded eleven GUSTO libraries as GDM status was not available for
eight samples, and three were identified as outliers (Supplementary Data 7; Supplementary Figure 6A).
We compared analyses using our placenta reference transcriptome versus GENCODE v45 and their union
(GENCODE+), adjusting for gestational age, infant sex, and technical variation estimated with RUVr73
(Figure 3E; Supplementary Figure 7A-B). Using FDR < 10% thresholds, our placenta reference
transcriptome revealed an average of 200 differentially expressed transcripts (DETs) between cohorts (SD
= 244.6) and 1,492 differentially expressed genes (DEGs; SD = 18.4), while GENCODE v45 yielded 333.5
DETs (SD = 99.7) and 914.5 DEGs (SD = 867.6) and GENCODE+ yielded 402.5 DETs (SD = 41.7) and 923 DEGs
(SD = 823.1). Analyses using our placenta reference transcriptome resulted in identification of fewer, but
a higher annotated percentage of, GDM-associated features (Tables S10-11). This enrichment, combined
with the reduced inferential uncertainty we observed across transcripts, suggests that our tissue-specific
annotation approach concentrates the analysis on biologically relevant transcripts while filtering out noise
from irrelevant or poorly annotated features.
Placental transcription mediates GDM effects on birth weight
We performed pathway enrichment analysis on differentially expressed transcripts (FDR < 10%) in GUSTO,
recognizing that different annotation approaches may capture distinct but potentially valid biological
signals (Supplementary Data 12). Our placenta reference transcriptome identified pathways enriched for
endocrine and signaling functions: hormone receptor binding (odds ratio = 101.89, adjusted p = 3.20 × 10-
3), growth factor activity (odds ratio = 48.42, adjusted p = 2.49 × 10-3), and receptor ligand activity (odds
ratio = 18.81, adjusted p = 2.49 × 10-3) (Supplementary Figure 7C). In contrast, GENCODE annotations
revealed pathways related to structural processes: PI3K-Akt signaling pathway (odds ratio = 8.19, adjusted
p = 7.42 × 10-7) and focal adhesion (odds ratio = 10.16, adjusted p = 4.23 × 10-6) (Supplementary Figure
7D). To determine which approach better captures functionally relevant changes that mechanistically link
GDM to birth weight outcomes, we performed causal mediation analyses.
Given the known impacts of GDM on birth weight, we examined whether some of these effects are
mediated by placental transcription in GUSTO using multi-mediator analysis. To address the high-
dimensional nature of transcript expression data while maintaining statistical power, we performed
principal component analysis on differentially expressed transcripts (FDR < 10%) and, for equal
comparison, selected the first PC principal components which cumulatively explained at least 75% of the
total variance in transcript expression as mediators74,75 (lr-assembly, PC = 5 [77.09% total variance];
GENCODE v45, PC = 8 [76.72%]; GENCODE+, PC = 6 [75.00%]). We addressed GDM-transcriptome
(exposure-mediator) and transcriptome-birthweight (mediator-outcome) confounding by adjusting for
maternal ethnicity, infant sex, and gestational age. We confirmed these principal components met our
75% variance threshold through sensitivity analysis (Supplementary Figure 8A-B; Supplementary Data 13).
In GUSTO, GDM was significantly associated with increased birth weight (total effect = 0.430, 95% CI
[0.131, 0.729], p = 0.005). Importantly, the indirect effect of GDM on birth weight through placental
transcription when quantifying short-reads against our placenta reference transcriptome was statistically
significant (0.155 [0.016, 0.293], p = 0.029), with approximately 35.8% of the total effect being mediated
through placental isoform expression (Figure 3F). Consistent with the pathway enrichment results, our
tissue-specific approach identified mediators with clear placental endocrine functions (CSH1, CDC25C),
aligning with the hormone and growth factor pathways detected in the enrichment analysis. In contrast,
when analyzing the same samples using either GENCODE v45 or GENCODE+, the proportion mediated by
placental transcript expression was smaller and not statistically significant (GENCODE v45: 0.095 [-0.057,
0.247], p = 0.219) (GENCODE+: 0.064 [-0.050, 0.178], p = 0.273) and these annotations produced more
heterogeneous mediators including several transcripts of uncharacterized genes.
To confirm that these observations were not merely driven by differences in annotation size, we quantified
the GUSTO short-read samples against GTEx transcript assemblies for subcutaneous adipose tissue and
cultured fibroblast cells, performing identical differential transcript expression (Supplementary Figure 8C)
and multimediator analyses (Supplementary Figure 8D-E; Supplementary Data 13) as for the other
annotations. Consistent with our observation that annotation size alone does not drive differences in
quantification uncertainty, quantification against annotations of different sizes yielded insignificant GDM-
birth weight mediation by isoform expression (subcutaneous adipose: PC = 1, 0.045 [-0.017, 0.106], p =
0.154) (fibroblasts: PC = 2, 0.049 [-0.031, 0.128], p = 0.230). These findings demonstrate that both
transcript discovery and filtration improve detection of biologically relevant pathway enrichments and
estimation of mechanistically relevant transcriptional changes that mediate clinically important outcomes.
Unannotated CSH1 isoforms mediate GDM effects on growth
We further identified specific gene isoforms that mediate the effect of GDM on birth weight (Figure 4A;
Supplementary Data 14). In GUSTO, 11 transcripts showed significant mediation effects (p < 0.05),
including ESPRESSO:chr17:12985:70 (0.021, 95% CI [0.001, 0.048], p < 2 × 10-16), a previously unannotated
isoform of CSH1 not annotated in GENCODE, accounting for approximately 13.4% of the effect of placental
transcription. Similarly, in Gen3G, 19 transcripts showed a significant effect, including
ESPRESSO:chr17:12985:62 (0.216 [0.071, 0.389], p < 2 × 10-16), a different previously unannotated isoform
of CSH1, accounting for approximately 24.37% of the effect of placental transcription (total effect of GDM
on birth weight = 0.132, indirect effect of placental transcription = 0.117). The identification of different
CSH1 isoforms mediating GDM effects across two independent cohorts illustrates the plasticity of
alternative splicing in response to genetic and environmental factors (Figure 4B). This population-specific
variation was unique to CSH1 in our placenta reference transcriptome: GENCODE-annotated transcripts
showed no significant cross-cohort differences in expression patterns.
Long-read annotations redistribute short-read assignments
To understand how differences in transcript annotations affect differential expression (and by extension,
causal mediation) analyses, we tracked changes in effect sizes in GUSTO between quantifications on
GENCODE v45 and our placenta reference transcriptome (Figure 5A). For CSH1, which exhibited the most
dramatic expansion in isoform number in our reference, one FSM isoform (ENST00000558284.1) only
showed significant differential expression associated with GDM when quantified against GENCODE (Figure
5B). While this isoform showed moderate expression in both references, it had notably higher expression
when quantified against GENCODE (Figure 5C), owing to a redistribution of reads to ENST00000558284.1
or to previously unannotated isoforms when the data is quantified with our reference (Figure 5D). In
contrast, GNAS—a known imprinted gene in placenta76 that exhibited one of the most dramatic
contractions in transcriptional complexity relative to GENCODE (77 isoforms reduced to 17 in our
reference)—showed the opposite pattern. Isoform ENST00000476196.5 showed a significant GDM
association when quantified against our placenta reference transcriptome but not against GENCODE
(Figure 5E), and increased expression with our reference (Figure 5F-G). These findings illustrate how both
adding unannotated transcripts and removing irrelevant transcripts from a transcriptome reference results
in the redistribution of reads among isoforms of a gene and to other genes. This redistribution is likely
beneficial, as it reduces noise by reassigning reads from spurious annotations, improving both
quantification accuracy and the detection of biologically relevant differential expression signals.

Methods
Ethics Approval
This research was approved by the National Healthcare Group Domain Specific Review Board, the
SingHealth Centralized Institutional Review Board, and the ethical committee of the Centre Hospitalier
Universitaire de Sherbrooke (CHUS), and complies with the Declaration of Helsinki. All three cohorts
recruited pregnant individuals assigned female sex at birth, and while we recognize that not all pregnant
individuals identify as women, analyses are based on placental samples from participants and their
newborns who were assigned male or female sex at birth. Informed written consent was obtained from
all mothers at recruitment, and all data are published in accordance with these approvals and consent
terms. Tissue donors for long-read sequencing (GUSTO and NUH cohorts) and GUSTO donors for short-
read sequencing were compensated for their participation, while Gen3G donors for short-read sequencing
were not compensated.
Study populations and cohorts
Samples were collected from term, live births without known placentation pathologies, such as
intrauterine growth restriction and pre-eclampsia. Samples for long-read sequencing were collected from
a subset of two prospective prebirth cohorts (n = 72; maternal age at delivery: 33.1 ± 3.5 years; maternal
ethnicity: 54% Chinese, 33% Indian, 13% Malay; gestational age at delivery: 38.6 ± 0.6 weeks; infant sex:
40 male and 32 female [determined at birth by clinical assessment]; birth weight: 3.3 ± 0.3 kg) with 50%
from pregnancies affected by gestational diabetes mellitus (GDM; categorized by a 75 g two-timepoint oral
glucose tolerance test using the WHO 1999 criteria which was in clinical use at that time).28,52,103 Of these
n = 72 samples, n = 6 were selected from the Growing Up in Singapore Toward Healthy Outcomes
(GUSTO28,103) cohort and have matched Illumina short-read libraries from the same biological specimens,
while the remaining n = 66 were selected from an independent National University of Singapore Hospital
(NUH52) cohort.
Samples for short-read sequencing were collected from GUSTO (n = 200; maternal age at delivery: 32.8 ±
4.8 years; maternal ethnicity: 70% Chinese, 15% Indian, 15% Malay; gestational age at delivery: 38.7 ± 1.2
weeks; infant sex: 106 male, 94 female [determined at birth by clinical assessment]; birth weight: 3.1 ± 0.4
kg) and Genetics of Glucose Regulation in Gestation and Growth (Gen3G,29,104 n = 152; maternal age at
delivery: 28.6 ± 4.3 years; maternal ethnicity: white European; gestational age at delivery: 39.3 ± 1.1
weeks; infant sex: 79 male and 73 female [determined at birth by clinical assessment]; birth weight: 3.4 ±
0.4 kg), with 20% from pregnancies affected by GDM.
Sample collection
Placentae were processed within 1 hr of delivery. Five villous biopsies of each placenta were rinsed with
phosphate buffer saline (PBS), snap-frozen with liquid nitrogen and stored at −80 °C before RNA extraction.
For the n = 6 GUSTO samples with long-read sequencing, RNA was extracted from approximately 100 mg
of crushed tissue using phenol-chloroform and purified with a Qiagen RNeasy Mini Kit. For the n = 66 NUH
samples, RNA was extracted following the same protocol as for the n = 6 from GUSTO, using approximately
100 mg of crushed tissue using phenol-chloroform and purified with a Qiagen RNeasy Mini Kit. Tissue
samples were consumed during these studies and are no longer available.
Paired-end Illumina libraries of villous placenta from term, live births from GUSTO and Gen3G were
generated from placental villous tissue samples collected after delivery and stored at -80 °C. For GUSTO
Illumina libraries, RNA was extracted using phenol-chloroform followed by purification with the
NucleoSpin RNA kit (Macherey-Nagel). RNA quality was assessed by Nanodrop spectrophotometry and
Agilent TapeStation, with ribosomal RNA depletion performed using Illumina Ribo-Zero kits. For Gen3G,
RNA was extracted using phenol-chloroform followed by purification with the NucleoSpin RNA kit
(Macherey-Nagel). RNA quality was assessed using an Agilent Bioanalyzer.
RNA sequencing
Extracted samples from the long-read cohorts were sequenced via 3x 24-plex on PromethION (release
23.04.6, Oxford Nanopore, ONT) with manufacturer's PCR-cDNA library prep kit (#SQK-PCB111.24) at the
Genome Institute of Singapore using manufacturer's protocols. Median read lengths were approximately
1,000 bp (vs. 150 bp via NovoSeq). In GUSTO, sequencing libraries were prepared using NEB Next Ultra
Directional RNA Library Prep Kit and sequenced on Illumina HiSeq 4000 platform to a minimum of 50 M
paired-end 150 bp reads per library. In Gen3G, libraries were prepared from 250 ng of RNA using Illumina
TruSeq Stranded mRNA Sample Preparation Kits and sent to the Broad Institute for sequencing on an
Illumina HiSeq 4000 platform to a minimum of 14 M paired-end 100-bp reads per library.
Transcriptome assembly
FAST5 files of the raw ONAT signals for each sample were processed with Guppy v.6.5.7 for base calling on
a GPU with barcode adapter trimming enabled. FASTQ files labeled “pass” were concatenated together
for each sample. Pychopper v2.7.9 was used to identify, orient and trim full-length cDNA reads containing
primer sequences used in library construction with the PCS111 sequencing kit (ONT). Full length and
rescued cDNA reads were concatenated for each sample, and microindels and sequencing errors were
corrected with FMLRC2105 v0.1.8 using short-read RNA-seq samples of villous placenta from GUSTO.28 The
corrected reads were aligned to the human genome assembly analysis set (GenBank assembly
000001405.15, no ALT contigs, hg38) using Minimap2106 with a kmer length of 14, minimizer window size
of 4, and in splice-aware mode with --splice-flank=no to relax assumptions about bases adjacent to
splice junction donors and acceptors. Secondary alignments were filtered, and primary alignments were
coordinate sorted with samtools v1.9.107 The filtered and sorted alignments to chromosomes 1-22, X, Y
and M for all samples were then concatenated together to generate a single BAM file used for de novo
transcript assembly with ESPRESSO v1.6.0, which shows favorable balance in maximizing discovery while
limiting false discoveries.49
Transcriptome annotation and filtering
The assembled transcript models were filtered using a custom R108 script to remove isoforms with un-
stranded exons (retaining only forward or reverse strand isoforms), isoforms with an exon less than 3
nucleotides long, and isoforms with exons positions outside the transcript start and end positions. We
then used SQANTI3109 v5.4 to characterize the de novo assembly and classify isoforms into structural
categories relative to GENCODE v45 transcripts: full splice matches (FSM), incomplete splice matches
(ISM), novel in catalog (NIC), novel not in catalog (NNC), and other classes (e.g. fusion products and
antisense transcripts). Open reading frames and coding sequences were predicted with TransDecoder
v5.7.1.110 Coding potential was calculated with CPC v2.0.63 For protein validation, we performed BLASTP64
v2.17.0 searches of predicted peptide sequences against two databases: (1) peptides identified by tandem
mass spectrometry (MS/MS) of placental tissue samples, including from GDM-affected pregnancies,65,66
and the full UniProt human proteome. To ensure isoform-specific validation, we extracted the matched
peptide sequences from each BLASTP hit and identified peptides unique to individual isoforms within each
gene. Only transcripts with at least one unique peptide match (> 95% sequence identity) and CPC2 coding
probability > 0.5 were considered validated. Tissue-specific gene enrichment was assessed with
TissueEnrich v1.26.0.53
For quality control, we considered long-read coverage of the assembled transcripts quantified with
ESPRESSO, in addition to splice junction coverage in the short-read RNA-seq samples, proximity of
transcription start sites (TSS) from Cap Analysis of Gene Expression sequencing (CAGE-seq) peaks,
genome-wide sequencing of regions sensitive to cleavage by DNase I (DNase-seq) peaks or reference TSS,
and proximity of transcription termination sites (TTS) from sequencing alternative polyadenylation (polyA)
sites (SAPAS), polyA motifs or reference TTS (Supplementary Data 16). We defined high-confidence
isoforms across all structural categories as those with a minimum of 3 long (Nanopore) reads covering the
transcript and at least 3 short (Illumina) reads covering each splice junction. We validated transcription
start sites (TSS) by requiring them to be within 5 kb of a CAGE-seq peak in the FANTOM5 placenta
dataset,111 within 5 kb of a DNase-seq peak in the ENCODE placenta dataset,19 or within 100 bp of a
reference TSS. Similarly, transcription termination sites (TTS) were required to be within 100 bp of a SAPAS
site in the APASdb placenta dataset,112 within 100 bp of a reference TTS, or within 40 bp of a PolyA motif
in the PolyA_DB dataset.113 For all categories except FSM, we required transcripts to have less than 80%
adenosine content in the 20 nucleotides at the 3’ end to eliminate artifacts of PolyA intrapriming.109 For
specific previously unannotated isoforms (excluding FSM, ISM and NIC), we applied additional filters:
removing transcripts with evidence of reverse transcriptase (RT) switching at splice junctions (a known
technical artifact) and applying additional scrutiny to transcripts predicted to undergo nonsense-mediated
decay (NMD) including substantial 5’ expression (defined as a ratio of > 0.5:1.0 between short-reads
mapping to the first and last splice junctions) to distinguish genuine NMD substrates from assembly
artifacts.
Preparation of orthogonal datasets for assembly filtering
Short-read RNA-seq reads from term, villous placenta from GUSTO were aligned to hg38 using STAR114
v2.7.11b with settings recommended by ENCODE. Splice junctions with < 10 supporting reads were
filtered. CAGE-seq reads from placenta tissue (n = 1) and cells (pericyte, n = 2; epithelial, n = 4, trophoblast,
n = 1) were retrieved from the FANTOM5 Project111, trimmed with cutadapt v4.1115 to remove linker
adapters, EcoP15 and 5’poly-G sequences, and filtered reads were aligned to h38 using STAR with settings
recommended by ENCODE (Supplementary Data 16). CAGE tags were counted and clustered using the
CAGEr116 v2.16.0 package in R. DNAse-seq peaks from placenta tissue (n = 22) mapped to h38 were
retrieved in BED format from the ENCODE consortium and intersected with bedtools v2.31.0.117 SAPAS
peaks in placenta tissue mapped to hg38 were retrieved in BED format from APASdb,112 and human polyA
motifs annotated in PolyA_DB113 were retrieved from PolyA-miner.118
Comparative Analysis of GTEx Tissues with Matched Processing Pipeline
To enable direct comparison of placental transcriptional complexity with other human tissues, we
reprocessed 15 GTEx47 v9 tissues and cell lines from raw Oxford Nanopore Technologies (ONT) long-read
RNA sequencing fastq files (retrieved from dbGaP, phs000424.v9.p2) using an identical bioinformatic
pipeline to our placental samples. Assembled transcripts were annotated using SQANTI3 with the same
reference files and parameters as placental samples, including GENCODE v45 comprehensive annotation
and the human polyA motifs database. For splice junction validation and transcript filtering, we utilized
tissue-matched short-read RNA-seq samples from GTEx (retrieved from dbGaP, phs000424.v9.p2) and
orthogonal datasets from multiple public repositories (FANTOM5, ENCODE, SRA, PolyA-Site) employing
diverse sequencing methods (CAGE, RAMPAGE, 3'-seq, SAPAS, PolyA-seq, DRS) (Supplementary Data 16).
These orthogonal datasets were processed identically to placental validation data. We applied identical
SQANTI3 filtering criteria to all tissues to retain high-confidence isoforms. Long-read sample sizes, read
depth metrics, and assembly statistics for all GTEx assemblies are provided in Supplementary Data 17.
Short-read RNA-seq analyses
Paired-end Illumina libraries of villous placenta from term, live births of the GUSTO and Gen3G were
sequenced previously. We used fastp119 v1.0.1 to trim sequencing adapters and filter low-quality reads.
Decoy-aware transcriptome indices for Salmon120 v1.10.2 for GENCODE v45 transcripts, our long-read
defined placental transcripts (lr-assembly), and their union (GENCODE+) were constructed with MashMap
v3.1.3.121 Transcriptome quantification was then performed with Salmon with settings --libType A --
validateMappings --seqBias and including 50 bootstrap replicates to estimate:
E(k) i,j (1)
the expression E of transcript T in sample S for B bootstrap replicates, where j = 1,…,n transcripts in the
given assembly, i = 1,…,n short-read samples, and k = 1,…,50 replicates. Additional quantifications were
performed against the long-read transcript assemblies of GTEx47 v9 subcutaneous adipose tissue and
cultured fibroblast cells following the same procedure described above.
Transcript quantifications were loaded into R with tximport v1.34.0,122 and overdispersion arising from
read-to-transcript mapping ambiguity was estimated with catchSalmon() from the edgeR v4.4.2 package25
and divided from the counts25 prior to downstream analyses. To reduce noise in downstream analyses, we
filtered the expression matrices to retain only transcripts with at least 5 read counts in a minimum of 3
samples. Pearson correlation coefficients were calculated for each pair of samples within each cohort on
the transcripts-per-million (TPM) normalized counts.
To test for differences in total transcript expression (TPMs) between assemblies, we used linear mixed-
effects models with assembly and cohort as fixed effects and sample as a random effect. To examine
differences in mean and variance properties between assemblies and transcript sets, we performed nested
ANOVAs using log-transformed mean or variance in expression as the outcome variable, with cohort-by-
transcript-set and cohort-by-assembly-set interaction terms. Post-hoc pairwise comparisons between
transcript sets within each cohort were conducted using estimated marginal means with Tukey adjustment
for multiple comparisons.
Differential transcript and gene expression analyses of GDM status were performed with DESeq2
v1.45.3.123 Models for all analyses adjusted for gestational age (scaled), infant sex and 2 dimensions of
technical variation estimated with RUVr from the RUVSeq v1.40.0 package73. Maternal sex was not treated
as a variable as all participants were female. Sex-stratified analyses were not a primary aim of this study,
and the cohorts were underpowered for such analyses. Associated sample covariates from the Gen3G
study are available via dbGaP (accession phs003151.v1.p1) and from the GUSTO study under restricted
access (see Data Availability). Expression count matrices are available in a Zenodo repository (Data
Availability), enabling sex-disaggregated analyses by qualified researchers with appropriate data access
permissions. Isoform-level mediation analyses124 of the GDM-associated differentially expressed
transcripts at Benjamini-Hochberg adjusted p-value (false discovery rate, FDR) <10% were performed with
the mediation v4.5.1 package125 using variance stabilizing transformation (VST) normalized counts to
estimate the indirect effect of GDM on birth weight (scaled).
Multi-mediator analyses were performed with the lavaan v0.6 package126 selecting as mediators, for equal
comparison, the first PC principal components74 that cumulatively explained at least 75% of the total
variance in the VST-normalized counts of the GDM-associated transcripts (DTEs). Models estimated the
total effect of GDM and the indirect effect of placental transcription on birth weight, adjusting for
gestational age and infant sex. To assess potential confounding by population structure, we used one-way
ANOVA to test for associations between each PC and maternal ethnicity, calculating p-values and eta-
squared effect sizes using the effectsize v1.0.1 package127 to quantify association strength.
Transcript-level Inferential relative variance (InfRV) across median-ratio scaled bootstrap replicates72 was
calculated as:
InfRV = max(s2-μ,0) / (μ+5) (2)
where s2 is the sample variance of scaled counts over bootstrap replicates, and μ is the mean scaled count
over bootstrap replicates, with a pseudocount of 5 added to the denominator and 0.01 added overall to
stabilize the statistic and make it strictly positive for log transformation. To visualize read mapping
transitions between assemblies for n = 10 randomly selected samples from GUSTO (with 50% from GDM
affected pregnancies), we aligned the short-reads with BWA-MEM2128 v0.7.19 to the transcript sequences
in each assembly with settings -O 12,12 -E 4,4 to prevent gapped alignments. End-to-end alignments
to the transcript sequences were then filtered with a custom Perl script to retain only the first mate in the
read pair and to remove secondary and supplementary alignments.
Statistics and reproducibility
No statistical method was used to predetermine sample size; the long-read cohort (n = 72) represents the
largest available collection of placental long-read RNA-seq samples, and short-read cohorts (GUSTO, n =
200; Gen3G, n = 152) were previously sequenced. Samples without known placentation pathologies were
included. For analyses of GDM effects, we excluded eleven GUSTO samples: GDM status was not available
for eight samples, and three were identified as outliers. The experiments were not randomized. The
investigators were not blinded to allocation during experiments and outcome assessment.
Statistical analyses were performed in R. Differential transcript and gene expression analyses were
conducted with DESeq2, adjusting for gestational age, infant sex, and 2 dimensions of technical variation
estimated with RUVr. Multiple testing correction used the Benjamini-Hochberg procedure (FDR < 10%).
Isoform-level mediation analyses were performed with the mediation package and multi-mediator
analyses with lavaan. Inferential relative variance (InfRV) was calculated across 50 bootstrap replicates
from Salmon quantification using edgeR::catchSalmon() to assess isoform quantification uncertainty.
All attempts at replication were successful.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Bresnahan and colleagues perform Oxford Nanopore cDNA long-read RNA-sequencing of 72 placenta samples and create a high-confidence placental transcriptome reference. This work is important, as the placenta is a very unique tissue that plays key roles in fetal development and diseases of pregnancy, yet very few isoform-resolved studies of the placenta exist. The authors put substantial effort into making sure their reference transcriptome includes only high-quality isoforms. Their cohort includes pregnancies with gestational diabetes mellitus (GDM) and their analyses suggest that achieving isoform-level resolution is able to explain more of the effect of disease on birth weight than does gene-level resolution. I believe this dataset is quite valuable, and while the manuscript is well written, I have some critiques and suggestions. I also thank the authors for sharing their code and documenting it so well!\nMajor comments:\n1. The authors assert that the placenta has previously been described as a “transcriptomic void”, and present results to challenge this. As far as I have understood, the authors’ main argument that the placenta is not a “transcriptomic void” is Figure 1D-E, where they compare their results to the latest GTEx long read data. In particular, Figure 1E shows that the mean isoforms per gene in the placenta is much higher than the mean isoforms per gene expressed in other tissues. I worry that these results are entirely due to the different ways these datasets were filtered and processed, but I did not find the relevant information in the Methods section to figure out how these very different datasets were compared. Ideally, the GTEx data should be re-processed from the raw ONT reads using a pipeline that is identical (or at the very least, extremely similar) to the pipeline used for the placental samples here. At the very least, more results comparing the GTEx data to the ONT data need to be presented: how do the GTEx samples compare in terms of number of individual patient samples per tissue, for example? What about read depth? Each of these could affect the total number of isoforms detected and thus cast doubt on Figure 1E. I also suggest adding some sort of saturation curve – perhaps the number of isoforms discovered given a certain number of patients or a certain level of gene expression in the placenta vs another GTEx tissue – as this would be more robust. The authors need to ensure this is added to the Methods section. Finally, I note that even if the more robust analysis does not show that the placental tissue has higher isoform diversity than other GTEx tissues, this does not hurt the novelty or importance of the paper, and it is still fair to pushback against calling the placenta a “transcriptomic void.”\n2. Substantial time is spent comparing how quantification of short-read RNA-seq data compares when using Gencode as the reference index vs the filtered placental transcriptome from their long-read data as the reference. This is an interesting and worthwhile analysis, but I believe it is important for the authors to include a third category: Gencode *merged* with their new placental transcriptome. The authors argue that the placental annotation “redistributes” short reads to the correct isoforms, as shown in Figure 3D. But long-read data is coverage-limited, so the possibility of the reverse also exists: if transcripts that are actually expressed are missing from the long-read annotation (due to lower coverage or stringent filtering, let’s say), then reads that *should* be assigned to these Gencode transcripts could be mis-assigned to long-read transcripts. Plus, is it not intuitive that inferential relative variance would be lower for an index annotation with fewer overall transcripts? The real negative control here would be to compare short read RNA-seq from placenta to a long-read defined transcriptome from another tissue, like one from GTEx. Another suggestion is to examine only splice-junction-spanning short reads that are obviously unambiguous in their assignments – how do these get assigned in Gencode vs LR-annotation?\n3. The high “validation” rate of novel transcripts via mass spec is extremely surprising. Individual transcripts can only be validated at the protein-level via unique peptides that map to unique exons, or exon-exon junctions, which should be very rare. The results presented here suggest that the authors are using *any* peptide that matches a transcript to count as a “validation” peptide, rather than unique junction-spanning peptides. Also, details of these analyses again need to be added to the Methods section.\nOther comments:\n1. Do the Illumina reads come from the same N=72 placental samples? I found it difficult to figure out whether these were sample-matched, or just tissue-matched.\n2. Language in lines 182-186 is a bit weird. Calling “NMD” transcripts problematic, the same way RT-switching or intrapriming artifacts are problematic, is incorrect. NMD transcripts can be real and biologically meaningful. Also, removing these could potentially cause incorrect read re-distribution (see my major comment 2 above).\n3. In the uncertainty histograms in Figure S4C,D, what is causing the strange bump in the green histograms? This seems problematic and should be addressed, as it could skew the inferential uncertainty results if it’s arising due to some unforeseen artifact (again see major comment 2 above).\n4. Using the term “recently evolved” (line 241) is a stretch without further evidence, such as synteny analyses. Are the novel genes mostly non-coding? If so, they could also be transcriptional noise and not under any selection.\n5. The authors could do substantially more in terms of presenting the isoforms that seem interesting in their analyses. Some suggestions: add an ORF/protein domain plot above the CSH1 isoforms in Figure 2 and Figure 4. It is difficult to see where the coding region starts and ends. Do these novel isoforms affect interesting domains? In Figure 4E, only a subset of CSH1 isoforms are presented, but it’s very difficult to figure out what is interesting about each one. Is there some quantitation that can be presented alongside each isoform, similar to the plots in 4C and 4D? Relatedly: I find 4C and 4D quite difficult to parse. Does the isoform-level effect also show up when plotting simply GDM vs. non-GDM isoform expression?\n6. Figure 4B – the error is quite high! Can the authors compare their indirect effect estimate to a LR-seq transcriptome generated from another tissue to serve as an additional comparison point? (I also mention this in major comment 2).\nMinor stuff:\n–Typo 903/904 “long-readss”\n–Lines 309-311 re-write this sentence, it doesn’t read well", "reviewer_2": "A descriptive, compendium of transcription in term placentae based on nanopore long read cDNA sequencing. The authors uncover substantial novel transcriptional activity, including placenta specific transcripts. This work, though largely descriptive, is an invaluable resource. The authors provide extensive comparison of their data with tshort read RNAseq, illustrating the benefits. There is perhaps less translational relevance than the authors might provide, but the placenta long read reference developed here significantly improved prediction of transcriptional mediation of GDM impact on birth weight and fetal growth. While this does not strike me as a must, if they have SNP genotype data on the placentae, they might look at some of the novel transcripts in the context of earlier work and placental gene expression linked with heritability of developmental disorders, such as autism and intellectual disability, among others.\nBeyond these general issues, I few clarifications are needed:\n- No information is provided about the RNA quality of the samples, and the amount of RNA degradation. It is not clear whether the detection of differentially expressed isoforms could be affected by differences in RNA degradation among samples. The potential impact of RNA degradation needs to be analyzed and discussed also because an impact of GDM on RNA degradation is possible.\n- Similarly, the potential impact of genetic ancestry on splicing and differential expression needs to be considered.\n- A table summarizing the characteristic of placenta samples (N, sex, RIN, gestational age, diseases, ancestry, etc.) would be useful.\n- Limitations of long-read sequencing need to be at least mentioned.\n- The manuscript is data-rich and dense with results. Consider guiding the reader with more explicit summary diagrams or visual models connecting clinical and molecular findings, and mechanistic hypotheses.\n- All the results are provided in the GitHub repository. However, a browser interface where to visualize the gene isoforms would increase the impact of this work.\n- A state of art full classification of the diverse species of coding and noncoding RNA should be used by the authors when they report their findings. If this is not possible across the manuscript, it should be done in one table.\nOther points:\n- Does offspring sex impact transcriptional complexity?\n- Reference 42 misses the journal (it should be Diabetes)", "reviewer_3": "Bresnahan et al. assembled a placenta-specific long-read transcriptome (Oxford Nanopore; term villous tissue) and generated a catalog of high-confidence isoforms, including numerous novel transcripts in endocrine and growth pathways. Using this reference to re-quantify independent short-read datasets, the authors showed reduced inferential uncertainty and identified isoform-level mediators, most notably CSH1 isoforms, linking gestational diabetes mellitus (GDM) to birth weight. The dataset and approach address a known gap in placental genomics and demonstrate how annotation choice can materially alter biological conclusions.\nThe placenta has been underrepresented in large consortia, and a tissue-matched, long-read–derived reference provides both a valuable community resource and a strong methodological demonstration. The emphasis on isoform-resolved analysis (rather than gene-level summaries) reveals plausible endocrine mechanisms consistent with the Developmental Origins of Health and Disease (DOHaD) hypothesis, and the ancestry-specific CSH1 mediators are intriguing. Overall, the contribution is significant and, to my knowledge, novel in scope for human placenta. However, several points should be addressed to strengthen the manuscript before publication.\nMajor:\n1. Scale of isoform discovery and quantitative support in the main text. The reported number of isoforms is unusually high. The authors have strong orthogonal checks (short-read junction support; CAGE/DNase for TSS; SAPAS/polyA motifs for TTS; artifact filtering), but much of this evidence is confined to the supplement. It would be helpful to (i) elevate the explicit per-junction short-read support thresholds and counts of novel junctions ≥k spanning reads into the main text; (ii) add a concise table summarizing support by structural class (FSM/ISM/NIC/NNC/novel genes); and (iii) include a brief sensitivity analysis showing how stricter junction-support cutoffs affect the novel-isoform count.\n2. Functional relevance of novel transcripts. Consolidate in the main text a one-paragraph summary of coding potential and proteomic/MS support for novel transcripts: the fraction predicted coding, peptide-validated, and likely non-coding. This will temper concerns about transcriptional noise and clarify which novel classes are most likely to be functional.\n3. Cross-cohort consistency of ancestry-specific mediators. For the CSH1 mediators that differ between cohorts, please report whether each “lead” isoform shows sub-significant but consistent directionality in the other cohort, and provide effect sizes/SEs. A small cross-cohort table would strengthen the interpretation as splicing plasticity rather than cohort-specific technicalities.\n4. Bulk tissue heterogeneity and scope. Note explicitly that bulk placenta mixes cell types since some isoforms may be cell-type-restricted. A short paragraph acknowledging this limitation and pointing to single-cell/spatial extensions (and earlier gestational stages) would help readers interpret isoform diversity and mediator specificity.\n5. Targeted bench validation. If feasible, consider targeted RT-PCR/Sanger validation for a small panel: select novel CSH1/PSG splice junctions and one novel gene. This would provide an anchor wet-lab confirmation for readers skeptical of long-read discovery at this scale.\nMinor:\n1. Move key QC thresholds (e.g., ≥N long reads per isoform; ≥M short-read spans per junction; promoter/polyA proximity rules) from supplement to Methods or Results for visibility.\n2. Clarify mediation model assumptions (no unmeasured mediator–outcome confounding); report the number of PCs used and add a brief rationale in the main text, with CIs for mediated proportions.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We sincerely thank the reviewer for their thorough and constructive evaluation of our manuscript. We are pleased that the reviewer recognizes the importance of this work for understanding placental biology and diseases of pregnancy, appreciates the rigor of our quality control procedures, and finds value in our isoform-resolved approach to understanding GDM effects on birth weight. We also appreciate the reviewer's positive feedback regarding our code documentation and data sharing practices. We have carefully addressed all the reviewer's critiques and suggestions, which have substantially improved the manuscript. Below, we provide detailed point-by-point responses to each comment, including descriptions of new analyses, revised figures, and clarified text. We believe these revisions have strengthened both the scientific rigor and clarity of the work.\nReviewer #1 (Comments):\nComment:\nThe authors assert that the placenta has previously been described as a “transcriptomic void”, and present results to challenge this. As far as I have understood, the authors’ main argument that the placenta is not a “transcriptomic void” is Figure 1D-E, where they compare their results to the latest GTEx long read data. In particular, Figure 1E shows that the mean isoforms per gene in the placenta is much higher than the mean isoforms per gene expressed in other tissues. I worry that these results are entirely due to the different ways these datasets were filtered and processed, but I did not find the relevant information in the Methods section to figure out how these very different datasets were compared. Ideally, the GTEx data should be re-processed from the raw ONT reads using a pipeline that is identical (or at the very least, extremely similar) to the pipeline used for the placental samples here. At the very least, more results comparing the GTEx data to the ONT data need to be presented: how do the GTEx samples compare in terms of number of individual patient samples per tissue, for example? What about read depth? Each of these could affect the total number of isoforms detected and thus cast doubt on Figure 1E. I also suggest adding some sort of saturation curve – perhaps the number of isoforms discovered given a certain number of patients or a certain level of gene expression in the placenta vs another GTEx tissue – as this would be more robust. The authors need to ensure this is added to the Methods section. Finally, I note that even if the more robust analysis does not show that the placental tissue has higher isoform diversity than other GTEx tissues, this does not hurt the novelty or importance of the paper, and it is still fair to pushback against calling the placenta a “transcriptomic void.”\nResponse:\nWe thank the reviewer for this thoughtful and constructive comment. We reprocessed the 15 GTEx tissues and cell lines from the original fastq files (retrieved from dbGaP, phs000424.v10.p2) using an identical bioinformatic pipeline to our placental samples. Specifically:\n(Lines 597-607): To enable direct comparison of placental transcriptional complexity with other human tissues, we reprocessed 15 GTEx47 v9 tissues and cell lines from raw Oxford Nanopore Technologies (ONT) long-read RNA sequencing fastq files (retrieved from dbGaP, phs000424.v10.p2) using an identical bioinformatic pipeline to our placental samples. Assembled transcripts were annotated using SQANTI3 with the same reference files and parameters as placental samples, including GENCODE v45 comprehensive annotation and the human polyA motifs database. For splice junction validation and transcript filtering, we utilized tissue-matched short-read RNA-seq samples from GTEx (retrieved from dbGaP, phs000424.v10.p2) and orthogonal datasets from multiple public repositories (FANTOM5, ENCODE, SRA, PolyA-Site) employing diverse sequencing methods (CAGE, RAMPAGE, 3'-seq, SAPAS, PolyA-seq, DRS) (Table S16). These orthogonal datasets were processed identically to placental validation data. We applied identical SQANTI3 filtering criteria to all tissues to retain high-confidence isoforms. Long-read sample sizes, read depth metrics, and assembly statistics for all GTEx assemblies are provided in Table S17.\n(Figure 1D): The number of isoforms and genes per tissue, with the number of reads used in assembly shown along the y-axis, providing transparency regarding sequencing depth differences across tissues.\nTranscriptional breadth across tissues\nn = assembled reads\n50K\nPlacenta (n = 118.6M)\nCells − Cultured Fibroblasts (n = 83.9M)\nBrain − Cerebellar Hemisphere (n = 48.5M)\nLung (n = 41.5M) 40K\nHeart Atrial Appendage (n = 41.3M)\nMuscle Skeletal (n = 41.0M)\nLiver (n = 37.3M) 30K\nBrain − Frontal Cortex (Ba9) (n = 22.9M)\nBrain − Putamen Basal Ganglia (n = 20.2M)\nHeart Left Ventricle (n = 20.2M)\n20K\nK562 (n = 7.6M)\nPancreas (n = 6.0M)\nBrain − Caudate Basal Ganglia (n = 3.5M)\nBrain − Anterior Cingulate Cortex (Ba24) (n = 2.8M) Isoforms 10K\nBreast − Mammary Tissue (n = 2.3M)\nAdipose − Subcutaneous (n = 1.8M) Genes\n0k 10k 20k 30k 40k 50k 100 10K 1M\nCount Isoform expression\n(Figure 1D): Transcriptional breadth across tissues, visualized by the count of long-read defined genes and isoforms in placenta compared to 15 tissues or cell lines in GTEx47 v9.\n(Figure 1E): Saturation curves for each tissue, plotting the number of isoforms detected (y-axis) against the number of reads assigned (x-axis).\n(Figure 1E): The number of isoforms detected at increasing read coverage thresholds across tissues (data for placenta shown by thick black line).\ndlohserht\nnoisserpxe\nta\ndetceted\nsmrofosI\nTranscriptional breadth across tissues\nn = assembled reads\n50K\nPlacenta (n = 118.6M)\nCells − Cultured Fibroblasts (n = 83.9M)\nBrain − Cerebellar Hemisphere (n = 48.5M)\nLung (n = 41.5M) 40K\nHeart Atrial Appendage (n = 41.3M)\nMuscle Skeletal (n = 41.0M)\nLiver (n = 37.3M) 30K\nBrain − Frontal Cortex (Ba9) (n = 22.9M)\nBrain − Putamen Basal Ganglia (n = 20.2M)\nHeart Left Ventricle (n = 20.2M)\n20K\nK562 (n = 7.6M)\nPancreas (n = 6.0M)\nBrain − Caudate Basal Ganglia (n = 3.5M)\nBrain − Anterior Cingulate Cortex (Ba24) (n = 2.8M) Isoforms 10K\nBreast − Mammary Tissue (n = 2.3M)\nAdipose − Subcutaneous (n = 1.8M) Genes\n0k 10k 20k 30k 40k 50k 100 10K 1M\nCount Isoform expression\ndlohserht\nnoisserpxe\nta\ndetceted\nsmrofosI\n(Figure 1F): The revised figure now displays the maximum isoforms per gene for each tissue in brackets along the y-axis.\nTranscriptional complexity across tissues\nPoints = mean; lines = ±1 SD; (max) shown above\nPlacenta (108)\nCells − Cultured Fibroblasts (68)\nBrain − Cerebellar Hemisphere (89)\nLung (58)\nHeart Atrial Appendage (49)\nMuscle Skeletal (47)\nLiver (52)\nBrain − Frontal Cortex (Ba9) (34)\nBrain − Putamen Basal Ganglia (30)\nHeart Left Ventricle (30)\nK562 (22)\nPancreas (94)\nBrain − Caudate Basal Ganglia (18)\nBrain − Anterior Cingulate Cortex (Ba24) (19)\nBreast − Mammary Tissue (24)\nAdipose − Subcutaneous (21)\n0 2 4 6 8 10\nIsoforms per gene\n(Figure 1F): Transcriptional complexity across tissues, visualized by the mean and standard deviation of isoforms per gene. Maximum values are indicated in brackets.\nOur reanalysis revealed important nuances that refine our original interpretation. Specifically, the reprocessed comparison using identical pipelines revealed that placental transcriptional breadth and complexity are comparable to other tissues, though the placenta displays exceptional splicing diversity for specific pregnancy-related genes. We have revised language throughout the manuscript to emphasize these differences. We also note the apparent relationship between transcriptional breadth, complexity, and sequencing depth as important considerations for future work:\n(Lines 7-10): Contrary to characterizations of the placenta as a \"transcriptomic void,\" we found transcriptional breadth and complexity comparable to adult tissues, with extraordinary splicing diversity in genes controlling obesity, lactogen production and growth, including 108 distinct CSH1 (placental lactogen) isoforms.\n(Lines 180-190): To evaluate placental transcriptional breadth and complexity with reference to other human tissues, we reprocessed 15 GTEx47 tissues and cell lines from raw ONT fastq files using an identical bioinformatic pipeline to our placental samples, including matched assembly, annotation, filtering, and orthogonal validation procedures (Tables S16-S17; Methods). Our analysis revealed that the placenta exhibits transcriptional breadth (total number of isoforms and genes expressed) comparable to other human tissues (Figure 1D). Saturation analysis demonstrated that isoform discovery plateaus at similar sequencing depths across all tissues, confirming that our assemblies achieved sufficient depth for robust comparison (Figure 1E). Transcriptional complexity (the mean number of isoforms per gene) ranged from 1.8 to 2.8 across tissues (Figure 1F). These findings challenge previous characterizations of the placenta as a “transcriptomic void”21,23,61 expressing few, high-abundance isoforms. Instead, the placenta exhibits transcriptional breadth and complexity comparable to other tissues and utilizes complex splicing regulation— particularly for genes involved in maternal-fetal signaling.\n(Lines 194-195): While transcriptional breadth and complexity of the placenta are comparable to other tissues, it exhibits exceptional splicing diversity in genes with critical functions in pregnancy and placental development.\n(Lines 388-392): Using matched bioinformatic processing of placental and GTEx47 tissues, our results demonstrate that the placenta exhibits transcriptional breadth and complexity comparable to other adult tissues while displaying exceptional isoform diversity for specific pregnancy-related genes. This pattern challenges previous characterizations of the placenta as a “transcriptomic void”21,23,61 and demonstrates that the placenta utilizes complex splicing regulation, particularly for genes involved in maternal-fetal signaling.\n(Lines 398-401): Importantly, transcriptional complexity and breadth metrics are sensitive to sequencing depth, with greater depth yielding more isoforms and genes until saturation is reached. This relationship underscores the importance of adequate sequencing depth and matched processing pipelines for robust cross-tissue comparisons, as depth disparities between studies may lead to spurious differences in apparent transcriptional complexity.\nWe agree with the reviewer's final point that even with these refined conclusions, our work provides important pushback against the characterization of the placenta as a \"transcriptomic void\" while also establishing a high-quality, isoform-resolved reference that enables more precise functional analyses.\nComment:\nSubstantial time is spent comparing how quantification of short-read RNA-seq data compares when using Gencode as the reference index vs the filtered placental transcriptome from their long-read data as the reference. This is an interesting and worthwhile analysis, but I believe it is important for the authors to include a third category: Gencode *merged* with their new placental transcriptome.\nResponse:\nWe appreciate the reviewer's suggestion and have included GENCODE+ (the union of GENCODE v45 and our placenta transcriptome) throughout our quantification analyses. Our results demonstrate that while merging annotations improves performance over GENCODE v45 alone, it does not achieve the same benefits as using the filtered placenta transcriptome (lr-assembly) for matched-ancestry samples. Specifically, in the GUSTO cohort (Singaporean), which shared ancestry with our long-read samples, lr-assembly outperformed both GENCODE v45 and GENCODE+ in total quantified expression, quantification precision (inferential relative variance), and statistical power for detecting GDM-transcript-birth weight associations. In the Gen3G cohort (European), GENCODE+ showed the highest total expression, likely due to capturing additional European-specific isoforms not present in our Singaporean-derived assembly. However, lr-assembly still demonstrated superior quantification precision (reduced InfRV) in both cohorts, indicating that tissue-matched transcript discovery and stringent quality filtering provide fundamental improvements beyond what can be achieved by simply combining annotations. These ancestry-specific patterns highlight both the value of our tissue-specific approach and the need for expanding long-read sequencing across diverse populations:\n(Lines 236-240): To evaluate the impact of transcript discovery and filtration on short-read transcript quantification, we compared quantifications against our placenta transcriptome reference, GENCODE v45 and their union (GENCODE+). We analyzed Illumina cDNA libraries from villous placental tissue collected in two independent pregnancy follow-up studies: GUSTO28 (n = 200) and the Genetics of Glucose regulation in Gestation and Growth (Gen3G29; n = 152), both with approximately 20% of samples from GDM-affected pregnancies (Figure 3A).\nRNA-seq GENCODEv45 …\nGen3G transcripts\n(n=152) A B\nDifferential\n!\n(%)\nlr-assembly (long-\nS\nU\nh\nn\no\n…m\nrt\na\n-\np\nre\npe\na\nd\nd mapping to GENCODEExShoprt-readr maepping sto lr-asssembilyon\n!,# read transcripts) > > > I > so > form >\nIncorrectly >> > >\nmapped eRxedisptributerdession\nRNA-seq\nGENCODE+\nGUSTO (v45 + novel …\n(n=200) GDM Birth weight\nMediation\n(Figure 3A): Illumina short-read RNA-seq of villous placental tissue from two independent pregnancy follow-up study cohorts with approximately 20% of samples from GDM-affected pregnancies were quantified against the GENCODE v45 transcripts, the high-confidence placenta transcripts (lr-assembly) and their union (GENCODE+).\n(Lines 244-253): Our placenta reference transcriptome demonstrated population-specific differences in total quantified expression that varied by cohort ancestry. In the GUSTO cohort (Singaporean), which shared ancestry with the samples used to generate the placenta reference transcriptome, lr-assembly achieved the highest total quantified expression with an increase of 7,351 log(TPM) over GENCODE v45 (17.9% increase, p < 2 × 10-16) and 4,622 log(TPM) over GENCODE+ (10.6%, p < 2 × 10-16) (Table S8). In contrast, the Gen3G cohort (white European) showed the highest total quantified expression with GENCODE+, which combines both annotation sources, with an increase of 13,044 log(TPM) over GENCODE v45 (31.2%, p < 2 × 10-16). The lr-assembly alone provided a smaller but significant 3,896 log(TPM) increase over GENCODE v45 in Gen3G (9.3%, p = 1.45 × 10-10). These differences in total quantified expression across cohorts (interaction p < 2 × 10-16) likely reflect the ancestry composition of our long-read samples, with GENCODE+ detecting additional isoforms in Gen3G that were not captured in the Singaporean-derived lr-assembly (Figure 3B).\n60,000\n40,000\n20,000\nGen3G (n=152) GUSTO (n=200)\n(Figure 3B): The total transcript expression (measured as transcripts per million [TPM]) per short-read sample in two cohorts (GUSTO, n=200; Gen3G, n=152) when quantified against each annotation.\n(Lines 255-257): Despite these ancestry-specific differences in total quantified expression, transcripts that failed to meet our stringent quality criteria showed significantly lower mean expression and variance in the short-read quantifications compared to FSM and novel transcripts in both cohorts (Figure 3C; Figure S6B).\n(Figure 3C): Comparing mean and variance in the GUSTO short-read samples across references for: transcripts that were shared between annotations (shared), transcripts that were novel in placenta (novel), constituting transcripts included in the placenta transcriptome reference (lr-assembly) through the discovery phase, in addition to GENCODEv45 transcripts that were initially recovered in the long-reads but removed in the filtering procedure (filtered) or that were not assembled from the long-reads (absent), constituting transcripts that were omitted from lr-assembly through the filtration phase.\n(Figure S6B): Isoform expression mean and variance properties in Gen3G when short-read samples were quantified against the placenta transcriptome reference (lr-assembly), GENCODEv45 or their union (GENCODE+).\n)MPT(gol\nΣ\nTotal isoform expression across samples\nGENCODEv45 GENCODE+ lr−assembly\nDiscovery Filtration\nMean\nVariance\nIsoform expression changes with transcript discovery and filtration\nData from GUSTO (n=200)\nGENCODEv45 GENCODE+ lr−assembly\n10K\n1K\n100\n10\n1\n100M\n100K\n100\nshared novel filtered absent\nDiscovery Filtration\nMean\nVariance\nIsoform expression changes with transcript discovery and filtration\nData from Gen3G (n=152)\nGENCODEv45 GENCODE+ lr−assembly\n10K\n1K\n100\n10\n1\n10M\n100K\n1K\n10\nshared novel filtered absent\n(Lines 270-274): Mean transcript-level InfRV was consistently reduced when short reads were quantified against our placenta transcriptome reference compared to both GENCODE v45 (30.4% reduced in GUSTO and 30.3% reduced in the Gen3G) and GENCODE+ (31.5% reduced in GUSTO and 30.4% reduced in Gen3G) (Figure 3D; Figure S6C), indicating improved isoform-level quantification precision.\nInferential uncertainty in isoform quantification\nDiamond = mode; data from GUSTO (n=200)\nGENCODEv45\nGENCODE+\nlr−assembly\n−2 −1 0 1 2\nlog10(mean InfRV)\n(Figure 3D): Inferential uncertainty (InfRV) in isoform quantification in the GUSTO short-read samples when quantified against transcripts in each annotation. Diamond = mode.\nInferential uncertainty in isoform quantification\nDiamond = mode; data from Gen3G (n=152)\nGENCODEv45\nGENCODE+\nlr−assembly\n−2 −1 0 1 2 3\nlog10(mean InfRV)\n(Figure S6C): Inferential relative variance (InfRV) in transcript quantification of Gen3G short-read samples.\n(Lines 300-306): We next examined transcriptional differences at gene and transcript levels associated with GDM status in GUSTO and Gen3G, comparing analyses using our placenta transcriptome reference versus GENCODE v45 and their union (GENCODE+), adjusting for gestational age, fetal sex, and residual variation estimated with RUVr73 (Figure 3E; Figure S7A-B). Using FDR < 10% thresholds, our placenta transcriptome reference revealed an average of 138 differentially expressed transcripts (DETs) between cohorts (SD = 159.8) and 792.5 differentially expressed genes (DEGs; SD = 955.3), while GENCODE v45 yielded 333.5 DETs (SD = 99.7) and 934.5 DEGs (SD = 839.3) and GENCODE+ yielded 471 DETs (SD = 138.9) and 923 DEGs (SD = 823.1).\n600\n400\n200\nGUSTOGENCODE+DEG\nGUSTOlr−assemblyDEG\nGen3GGENCODE+DET\nGen3GGENCODEv45DET\nGUSTOGENCODE+DET\nGen3GGENCODEv45DEG\nGen3GGENCODE+DEG\nGUSTOGENCODEv45DET\nGen3Glr−assemblyDET\nGen3Glr−assemblyDEG\nGUSTOlr−assemblyDET\n(Figure 3E): The count of differentially expressed genes (DEGs) and transcripts (DETs) in each cohort given short-read quantification against each annotation.\ntnuoC\nDifferential expression across cohorts and annotations\nDisplaying sets of n≥10 features"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have more than sufficiently addressed all of my concerns. I commend them on a very thorough and well written rebuttal. Kudos!", "reviewer_3": "The authors have satisfactorily addressed all of my comments. I have no additional concerns.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

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