The technical foundation of the study is what sets it apart. Conventional short-read RNA sequencing, the workhorse of most large cancer atlases including TCGA, chops RNA molecules into fragments of a few hundred bases and then computationally reconstructs transcripts by aligning those fragments to a reference genome. This approach excels at quantifying aggregated gene-level expression but routinely collapses the identity of individual full-length transcripts. Long-read sequencing, by contrast, reads entire RNA molecules from end to end, preserving the complete exon chain of each transcript. The researchers applied Nanopore-based long-read RNA sequencing to their tumor-normal pairs and subjected the resulting data to stringent quality filtering to remove outlier specimens. Validation was multi-pronged: the transcript read-length distribution confirmed full-length capture, the 5-prime ends of reads aligned with known CAGE peaks marking transcription start sites, the 3-prime ends showed expected poly-A motif occupancy, and cross-platform correlation against matched short-read RNA-seq data confirmed that gene-level quantification was technically sound.

From this carefully validated dataset, the team detected nearly 300,000 isoforms, and a substantial proportion of them represented previously unannotated entities. These included novel splice variants never cataloged in reference annotations as well as tumor-associated fusion transcripts, hybrid molecules created when genomic rearrangements join parts of different genes. Across the cancer types examined, the researchers recovered thousands of tumor-specific isoforms covering all seven major classes of alternative-splicing events, alongside hundreds of recurrent fusion transcripts. Critically, the authenticity of a subset of these fusion transcripts was supported by partial experimental validation using PCR followed by Sanger sequencing, a gold-standard confirmation method that lends credibility to the computational discoveries.

The study’s headline discovery emerges from pan-cancer comparative analysis, which exposed two uncoupled layers of transcriptomic dysregulation. At the gene expression level, tumors arising from diverse organs displayed convergent transcriptional signatures. In other words, a liver tumor and a lung tumor, despite their radically different origins, tend to switch similar sets of genes up or down. This convergence is tightly linked to oncogenic dedifferentiation, the cellular reversion toward stem-like phenotypic states that is a hallmark of aggressive malignancy. Many of the genes convergently down-regulated across multiple tumor types participate in tissue-specific physiological metabolism and ion homeostasis, functions that specialized cells abandon as they lapse into a primitive, proliferative program.

Isoform-level remodeling told a completely different story. Rather than converging, isoform composition shifted in a highly divergent, organ-restricted manner, with each tissue type exhibiting its own characteristic splicing perturbations. The most compelling evidence came from cross-organ correlation analysis: the correlations between isoform ratios across different tumor types resembled those observed in normal tissues, contradicting the heightened inter-tumor correlation seen for gene expression. This key observation demonstrates that widespread isoform shifts occur largely independently of bulk messenger-RNA abundance changes. Because conventional short-read workflows mostly quantify aggregated gene-level counts rather than individual full-length transcripts, this entire dimension of cancer biology has been systematically invisible to most previous pan-cancer investigations.

What drives this extensive isoform reprogramming? One major force is the dysregulation of splicing factors, the proteins that decide which exons are included or excluded during RNA processing. The researchers built a random-forest regression model trained on paired tumor-normal expression profiles to quantify how predictive the expression of more than four hundred splicing-factor genes was for genome-wide isoform-composition shifts. The analysis pinpointed a small subset of high-impact regulators, including HNRNPC and RBFOX2, both of which are already known to govern alternative-splicing circuits in cancer. The machine-learning approach thus provides a quantitative ranking of which splicing regulators exert the strongest influence on the isoform landscape in each tumor context.

Perhaps the most clinically provocative finding concerns genes whose isoform ratios change dramatically even when their total expression remains perfectly stable. The gene DUT, which encodes an enzyme involved in nucleotide metabolism, exemplified this pattern: pronounced isoform compositional remodeling took place in liver tumors while total gene transcript abundance stayed flat. Such cases underscore a critical limitation of short-read-oriented pan-cancer research. Many oncogenic transcriptomic events manifest exclusively through altered transcript-isoform proportions and structural variants, and they are simply invisible when analytical pipelines collapse all sequencing reads onto aggregated gene models. A cancer study that only measures gene expression is, in effect, listening to the average volume of an orchestra while ignoring which instruments are playing.

The authors are appropriately careful about the limits of bulk sequencing. All long-read measurements in this study represent population-averaged signals derived from heterogeneous tissue biopsies containing malignant cells, stromal cells, and immune infiltrates. Consequently, detected isoform alterations and fusion transcripts cannot be definitively mapped solely to cancer cells; some shifts may partly reflect variations in tumor purity and cellular composition within specimens. Even so, sensitivity analyses using deconvolution approaches confirmed that the core isoform-ratio trends persisted irrespective of purity fluctuations. The exact cellular origin of most detected transcript-level aberrations awaits future single-cell long-read RNA sequencing experiments, which would resolve intratumoral heterogeneity and assign molecular events to defined cell subpopulations.

To translate these multi-layered transcriptomic dimensions into biologically meaningful priorities, the team constructed a custom pan-cancer scoring framework. The framework merges gene-level differential expression, convergent-divergent variance patterns, tumor-specific isoform occurrence, fusion-transcript evidence, and isoform-ratio perturbations, supplemented with weighted evidence retrieved from TCGA and the Cancer Gene Census database. Each gene received an organ-specific score capped at one, and these were aggregated across all ten organs to compute a final pan-cancer susceptibility score. More than ten thousand genes were scored and stratified into low-, medium-, and high-score groups. High-score entries included well-characterized canonical cancer drivers such as TP53, RB1, and FAT4, providing an internal sanity check for the method.

More notably, the scoring system unearthed under-appreciated susceptibility genes whose dysregulation is dominated by isoform-level defects rather than total gene-expression change. Representative examples include IBSP, involved in extracellular-matrix organization and metastasis, and FANCL, which participates in DNA-damage-repair pathways; both display recurrent tumor-specific isoforms across multiple cancer types without prominent gene-level dysregulation. Kaplan-Meier survival analysis demonstrated that selected high-score genes carried significant prognostic implications, whereas near-zero-score genes showed no survival association, confirming the framework’s capacity to nominate prognosis-relevant molecules. The authors caution that the resource is not intended for early-cancer screening or treatment-response prediction, given the cohort’s lack of complete clinical follow-up metadata. Even with that caveat, the study delivers a public atlas for isoform-centered cancer research and a shortlist of actionable candidates for downstream functional validation, biomarker development, and therapeutic targeting, while making a forceful case that the next generation of cancer genomics must read transcripts in full.

Subject of Research: Pan-cancer long-read transcriptomics of gene and isoform expression changes in tumorigenesis

Article Title: Long-read pan-cancer transcriptomics unravel distinct alteration trends between gene and isoform expression in tumorigenesis

Article References: Long-read pan-cancer transcriptomics unravel distinct alteration trends between gene and isoform expression in tumorigenesis. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: long-read sequencing, Nanopore, pan-cancer, isoform expression, alternative splicing, fusion transcripts, tumorigenesis, splicing factors, transcriptomics, cancer biomarkers, dedifferentiation, RNA sequencing

Alpha-hemolysin is an exotoxin secreted by the bacterium Staphylococcus aureus. In its active form, the protein is built from 293 water-soluble amino acids that assemble into a heptameric pore, a seven-part barrel that punches through cell membranes. That structure, first resolved in crystallographic detail in the 1990s, has made alpha-hemolysin the workhorse of stochastic sensing: the first and still most widely used biological nanopore for detecting individual molecules. When a single pore is embedded in an artificial membrane bathed in electrolyte and a voltage is applied, a steady ionic current flows through the opening. When an analyte molecule wanders into the pore, it partially obstructs that current, producing a characteristic downward blip. Each blip corresponds to one molecule, and the pattern of blips over time carries information about the analyte’s identity and concentration.

In the new experiments, the team used monodisperse polyethylene glycol of molecular weight 1294, abbreviated PEG 1294, as the analyte. The experimental platform was a solvent-free planar lipid bilayer assembled by the classic Montal and Mueller technique: two monomolecular lipid films were apposed across a hole roughly 100 micrometers in diameter in a Teflon partition, forming a barrier that separated two identical compartments of a Teflon chamber. The synthetic lipid 1,2-diphytanoyl-sn-glycero-3-phosphocholine was used at 2 percent by weight in hexane, and the hole was pretreated with a dilute hexadecane solution to promote stable bilayer formation. Both compartments held a concentrated electrolyte of 4 molar potassium chloride buffered with 5 millimolar Tris-citric acid at pH 7.5. Alpha-hemolysin was added to one side, the cis side, at a concentration tuned so that exactly one pore inserted into the membrane.

The researchers then added PEG 1294 to the opposite, trans side at two concentrations, 400 and 1000 micromolar, and applied voltages ranging from +20 to +100 millivolts in 20 millivolt steps. Ionic currents were recorded with an Axopatch 200B amplifier in voltage clamp mode, using silver/silver chloride electrodes connected through agar salt bridges. The signals were low-pass filtered at 15 kilohertz and digitized at a sampling rate of 250 kilohertz, all at a controlled temperature of 25 degrees Celsius. Under these conditions, the current trace showed the textbook signature of stochastic sensing: stepwise transitions between a fully open level and a partially blocked level, each step corresponding to the entry or exit of a single PEG molecule from the pore lumen.

The novelty of the study lay not in the electrophysiology but in the analysis. Rather than focusing on conventional metrics such as event duration or blocking depth, the team turned to Lempel-Ziv complexity, a measure introduced in 1976 by Abraham Lempel and Jacob Ziv to quantify the randomness of finite sequences. The method works by converting a time series into a binary string: each data point is compared with the average of the entire series, receiving a 1 if it exceeds the average and a 0 if it falls below. The resulting string of ones and zeros is then scanned from left to right, and a counter increments every time a new, previously unseen substring appears. Finally, the counter is normalized by a theoretical upper bound, n divided by the base-2 logarithm of n, yielding a complexity value that ranges from 0 to 1 and is independent of sequence length.

The interpretation of this number is intuitive. A value close to 1 indicates a highly random, unpredictable signal with little internal self-similarity, while a value close to 0 indicates a repetitive, ordered series. Lempel-Ziv complexity has previously found applications in biomedical signal analysis, including studies of electroencephalogram background activity in Alzheimer’s disease patients and the detection of ventricular tachycardia and fibrillation in cardiac recordings. Applying it to nanopore data, however, allowed the researchers to ask a question that conventional event statistics cannot easily answer: how complex, in an information-theoretic sense, is the pattern of molecular blocking events?

The answer depended strongly on how much analyte was present. At the lower PEG concentration of 400 micromolar, complexity values were consistently lower than at 1000 micromolar, across every voltage tested. The full range of measured values stretched from a minimum of 0.45, recorded at 400 micromolar and +40 millivolts, to a maximum of 0.97, recorded at 1000 micromolar and +80 millivolts. The physical explanation is straightforward: higher analyte concentrations produce more frequent blocking events, filling the time series with rapid alternations between open and blocked states and pushing the complexity measure toward its upper limit. At lower concentrations, longer stretches of open-pore current dominate, and the signal becomes more ordered and less random.

To probe whether the apparent order at low concentration reflected genuine structure or simply sparse sampling, the researchers performed a clever control. They segmented the original time series recorded at 400 micromolar, shuffled the segments into a random order, and recalculated the complexity of the resulting randomized series. For every applied potential, the shuffled data yielded higher Lempel-Ziv values than the original recordings. This result is diagnostic: if the original series had already been maximally random, shuffling it would have changed nothing. The fact that randomization increased complexity means the unshuffled sequences contained residual structure, and that the biosensor’s output at low analyte concentration falls short of full stochastic behavior.

The team also compared series built exclusively from blocked periods with series built exclusively from unblocked periods, at both concentrations and all voltages. Statistical analysis using Student’s t-test, with a significance threshold of p less than 0.05, found no meaningful differences between the complexity of the blocked-only and unblocked-only series, nor between those segmented series and the original combined recordings. In other words, the complexity of the signal does not reside preferentially in either the moments when PEG occupies the pore or the intervals between occupations; the stochastic character is distributed across the entire record.

The broader implications reach into a long-standing debate in biology about the role of randomness. Molecular biology has traditionally leaned on deterministic principles, yet stochasticity is now recognized as a genuine feature of living systems rather than mere noise. Ion channel kinetics are widely treated as stochastic events, and some theorists argue that intrinsic randomness in the nervous system may even enable flexible decision-making. As the authors note, biological studies often lack the tools to distinguish chaotic, noisy, deterministic, and probabilistic behavior. Lempel-Ziv complexity offers one such tool. Their conclusion is nuanced: the alpha-hemolysin biosensor behaves in a purely stochastic fashion only at higher analyte concentrations, while at lower concentrations its stochasticity diminishes, though it can be mathematically restored by randomizing the event sequence. For engineers designing nanopore sensors, that finding matters, because the reliability of single-molecule detection depends on understanding when the digital current signature is truly random and when it carries hidden structure that smarter algorithms might exploit.

Subject of Research: Stochastic analysis of alpha-hemolysin nanopore ionic current blocking events using Lempel-Ziv complexity

Article Title: Analyzing alpha-hemolysin nanopore behavior using Lempel-Ziv complexity

Article References: Neves, G. F., Machado, D. C., Consoni, L. H. A., Costa, E. V. L., Carneiro, C. M. M., Rodrigues, C. G., & Nogueira, R. A. (2026). Analyzing alpha-hemolysin nanopore behavior using Lempel-Ziv complexity. Discover Chemistry, 3(1), Article 571. https://doi.org/10.1007/s44371-026-01022-8

Image Credits: AI Generated

DOI: 10.1007/s44371-026-01022-8

Keywords: alpha-hemolysin, nanopore, Lempel-Ziv complexity, stochastic sensing, polyethylene glycol, ion channel, planar lipid bilayer, biosensor, information theory, single-molecule detection, biophysics, stochasticity

The scaffold they chose was CsgG, a nonameric bacterial secretion pore that has found wide use in sensing applications because of its stable, well-characterized conductance. The challenge was formidable: the new designed components had to fit inside a confined, nonuniform space governed by ninefold rotational symmetry, and they had to do so without collapsing the channel. A functioning pore needs an open conducting lumen and a stable, low-noise baseline current; any design that distorted the assembly or obstructed the pathway would be electrically useless. The team’s solution was to design new protein subunits, which they named CsgX, that integrate seamlessly with the native CsgG ring, producing semisynthetic conduction pores containing eighteen subunits in total and weighing approximately 315 kilodaltons.

The computational approach leaned heavily on structural databases and established protein design tools. Using the MASTER fragment-search method, the designers first identified helical segments from the Protein Data Bank that could pack against an anchor helix derived from CsgF, a natural peptide that associates with the CsgG pore. Sequential searches of a non-redundant set of structures yielded roughly one thousand structural matches, which were clustered by root-mean-square deviation, and the geometrically recurring pairs were carried forward. Ninefold symmetry was then applied to generate complete assemblies, and only clash-free models with appropriate lumen geometry, in the range of roughly 19 to 23 angstroms in diameter, advanced to designability scoring. From there, loop-building connected the helices into helix-loop-helix motifs, and three backbone models were selected on the basis of structural prevalence, interface geometry and feasibility, with poorly packed or sterically occluded candidates excluded.

Interestingly, the team also attempted to generate the same CsgX-like topologies with RFdiffusion, the deep-learning-based protein design method that has transformed much of the field. Those attempts largely failed in a revealing way: the outputs either folded toward the CsgG body rather than into the lumen, or simply extended the anchor helix instead of creating the desired hairpin. The authors note that with more careful tuning this might be overcome, but they instead favored the fragment-based route, which allowed them to design against interfaces as close to optimal as the available structural data permitted, while keeping the design process transparent enough to extract physical principles for constructing functional pore architectures.

The designed variants came in two flavors, linear and cyclized versions of two distinct CsgX designs, CsgX1 and CsgX2, which differed in their engineered hydrogen-bonding networks. In CsgX1, intermolecular hydrogen bonds were designed into the helix-loop-helix motif, with hydrophobic interactions anchoring the C-terminal region; in CsgX2, the hydrogen-bonding network within the motif was intramolecular, while intermolecular contacts were reserved for the C-terminal region. These subtle differences in stabilizing interactions gave the team a set of closely related but structurally distinct pores with which to probe how lumen architecture translates into electrical behavior.

Structural validation came from cryo-electron microscopy. The team determined structures of four complexes, deposited in the Protein Data Bank and Electron Microscopy Data Bank under identifiers including 9PNA, 9PN8, 9PN9 and 9PNB, and the resulting maps confirmed the designed lumen architecture. For the CsgX1 variants and the cyclized CsgX2 design, density was observable for the full designed hairpin, demonstrating that the computationally specified helix-loop-helix motifs fold as intended within the confined pore interior. The one exception was the linear CsgX2 construct, where no density was visible beyond residue 30, precluding structural elucidation of its designed hairpin. The cryo-EM workflows, processed with tools including MotionCor2, Scipion and cryoSPARC, showed the usual mixture of single pores and pore dimers in two-dimensional class averages, with the cyclized CsgX2 variant uniquely showing only single pores.

The electrical measurements were where the designs truly proved themselves. Single-channel recordings performed in Oxford Nanopore Technologies MinION flow cells, using LSK114 sequencing buffer at 180 millivolts, showed that the semisynthetic pores insert into synthetic membranes and conduct ions with stable, low-noise open-pore currents. Crucially, the complexes exhibited distinct current-voltage responses relative to the native pore, including clear current rectification, meaning the pores conduct ions more readily in one direction than the other. Rectification is a valuable property in sensing because it can sharpen the electrical signature of molecules passing through the channel, and the fact that it emerged from designed subunits rather than from mutations to the native pore demonstrates that the approach can genuinely reprogram a pore’s electrical personality.

The broader significance of the work lies in the design philosophy. Natural multi-subunit pores are constrained by evolution, and modifying them through mutation alone offers only incremental control over lumen geometry and chemistry. De novo design of entirely new transmembrane pores, while advancing rapidly, still faces enormous challenges in achieving the stability and low-noise conductance that practical sensing demands. The semisynthetic strategy occupies a powerful middle ground: it preserves the proven conductance and assembly behavior of a native scaffold while allowing designed components to reshape the channel interior on a large scale. The successful integration of nine designed subunits into a working eighteen-subunit complex, under stringent symmetry and structural constraints, establishes a general blueprint for modifying existing nano-assemblies with custom protein parts.

The implications for nanopore sensing could be substantial. Because conductance depends directly on lumen geometry and chemistry, the ability to install designed subunits with tailored internal surfaces opens the door to pores optimized for specific analytes, from DNA and RNA to proteins and small molecules, a direction the field is actively pursuing in the push toward single-molecule proteomics. The design scripts used in the study have been released via Zenodo, and the structural models are publicly available, lowering the barrier for other groups to adopt and extend the approach. With support from the National Institutes of Health, the National Science Foundation and Oxford Nanopore Technologies, and with several of the authors named as coinventors on a related patent application, the work sits at the intersection of open science and commercial development, and it signals that the next generation of nanopores may be built as much by design as by evolution.

For a field that has spent three decades refining natural pores for sequencing and sensing, the demonstration that de novo protein components can be grafted onto a native pore complex and validated at atomic resolution marks a conceptual shift. The semisynthetic pores described by Schnaider and colleagues are not merely modified versions of CsgG; they are hybrid machines in which designed and natural protein parts cooperate to produce electrical behavior that neither could achieve alone. As the design toolkit matures, the same strategy could be applied to other symmetric nano-assemblies, extending the reach of computational protein design into some of the most demanding and commercially important structures in biotechnology.

Subject of Research: De novo protein design of semisynthetic nanopore conduction pores based on the CsgG scaffold

Article Title: De novo design of semisynthetic conduction pores

Article References: Schnaider, L., Hatstat, A. K., Scott, A. J., Tan, S. K., Hambley, R. G., Dawson, W. M., Griffiths, R. C., Kormos, R. C., Melo, A. A., Tse, E., Polizzi, N. F., Wallace, E. J., Merz, G. E., & DeGrado, W. F. (2026). De novo design of semisynthetic conduction pores. Nature Structural & Molecular Biology. https://doi.org/10.1038/s41594-026-01881-w

Image Credits: AI Generated

DOI: 10.1038/s41594-026-01881-w

Keywords: nanopore, protein design, de novo design, CsgG, cryo-EM, ion channels, current rectification, semisynthetic pores, structural biology, molecular sensing, DNA sequencing, protein engineering