Combinatorial and Inducible CRISPRa/i Enables Canalized hiPSC Forward Programming and Iterative Refinement via Single-Cell Genomics

Federica Sozza, Alberto Romano, Nicole D’Elia, Martina Terenzi, Maria Luisa Ratto, Emily R. Cliff, Gabrielle Nattenberg, Sara Bianchi, Silvia Becca, Heather Klug, Davide Cacchiarelli, Jesse G. Zalatan, Elisa Balmas, Alessandro Bertero

Posted on: 1 July 2026

Preprint posted on 1 June 2026

Activating and repressing in the same cell: the dream came true in human induced pluripotent stem cells.

Selected by Cell-ID

Categories: bioengineering, cell biology, genetics, genomics, molecular biology, synthetic biology

As part of a science communication workshop at the recent, annual Cell-ID meeting, this preprint was highlighted by: Emile Alghoul, Shaswati Sarbagna, Solène Dufau, Nada Abdelgawad, Valeriia Smialkovska, Asya Sayin, Iván Barberá-Aura, Rocío Nuñez-Vazquez, Jan Elliot González Álvarez, Delphine Burlet, Jane Schadtler-Law, Carla Piqueras, Maëlle Monier, Chloé Samblas, Cassandra Konan, Guillaume Sutty, Samir Bertache, Ajit Roy, Julien Leclercq, Davide Erba, Ana Boranijasevic, Cristina Fracassi, Iris Unterweger, Camille Bacquié, Charlene Clot, Stefany Figueroa, Chiara Gino, Claudie Carron

Why we highlight this preprint

This preprint tackles a significant challenge in the stem cell and epigenome engineering fields by introducing a scalable, flexible, and robust CRISPR-based platform for human iPSCs. The authors overcome major technical barriers that have limited CRISPR regulation in these cells, including transgene silencing and the difficulty of achieving stable gene control during differentiation.

By redesigning the system architecture around guide RNA–mediated recruitment of activators and repressors, they enable simultaneous control of multiple genes within the same cell using a single inducible dCas9 platform. This provides tunable, multiplexed, and bidirectional gene regulation.

This level of control opens new avenues for precise cell fate engineering, functional genomics, developmental biology, organoid research, and scalable forward programming in human models. Beyond its technical elegance, the platform introduced in this preprint stands out for its accessibility and adaptability for both basic and translational research communities.

Key findings

Combinatorial activation and repression approach in induced human pluripotent stem cells

The authors developed a system based on RNA oligos combining CRISPR guide sequence and recruitment site for multiple RNA binding effector proteins (preprint Figure 1E).

Top part of graphical abstract prepared by the preprint authors and made available under a CC-BY-NC 4.0 International license.

An inducible genome editing tool for pluripotent cells

The CRISPR tool developed by the preprint authors allows the generation and maintenance of stable hIPSC cell lines that allow CRISPR activation (CRISPRa) and CRISPR interference (CRISPRi) in an inducible way.

The developed tool was shown to be efficient at activating and inhibiting target genes in in vitro cell cultures (preprint Figure 3J).

Simultaneous gene activation and silencing for stem cell fate reprogramming

The authors combined inducible CRISPRa and CRISPRi modules and termed this platform CIRI: combinatorial inducible CRISPR in hIPSCs. For the first time, CIRI enables the coupling of transcriptional modulators.

Bottom part of graphical abstract prepared by the preprint authors and made available under a CC-BY-NC 4.0 International license.

By using CIRI to activate MYOD and repress canonical pluripotency genes, the authors could show that they can reprogram hIPSCs into skeletal muscle cells. Combinatorial activation and repression allowed more complete differentiation (preprint Figure 5E).

Identification of new drivers for myogenic differentiation by single-cell RNA-seq

The authors showed that activating KDM6B and repressing ID3 is a highly effective combination for myogenic differentiation. As such, this study provides a proof of concept that, without adding RA, CIRI is suitable for controlled differentiation workflows.

Programming cells more efficiently when manipulating more genes

Activation of MYOD1 + SMARCD3 and inactivation of OSN + ID3 turned out to be the best combination to permit more efficient survival of myogenic cells without retinoic acid. The study shows we can program iPSCs to any cell type thanks to genetic manipulation. It represents a breakthrough in cell genomic engineering for regenerative medicine.

Questions for the authors

Can multiple effector proteins be recruited to the same genomic site simultaneously, and how does this affect gene regulation dynamics?
Could this system incorporate additional types of effectors beyond activators/repressors (e.g. chromatin architectural proteins)?
Why was myogenic differentiation chosen as the primary model, and how well does the approach translate to other systems?
What is the genome-wide specificity of the system, and how significant are off-target or secondary transcriptional effects?
How tuneable is the strength of activation or repression for individual targets?

Tags: cell differentiation, combinatorial screening, crispra/i, epigenetic regulation, forward programming, gene regulation, hipscs, myogenesis, single cell rna sequencing, synthetic biology

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Author's response

Elisa Balmas and Alessandro Bertero shared

Thank you very much for highlighting our preprint and for sharing these thoughtful questions from the workshop participants. We were delighted to hear that the work stimulated discussion at Cell-ID.

Can multiple effector proteins be recruited to the same genomic site simultaneously, and how does this affect gene regulation dynamics?

CIRI was designed around the idea that the targeting module and the effector module can be separated. In the current implementation, a single dCas9 chassis is directed by engineered sgRNAs carrying orthogonal RNA scaffolds, which in turn recruit distinct RNA-binding protein–effector fusions. This allowed us to activate one set of endogenous loci while repressing another set in the same cell.

Recruiting multiple effectors to the same locus is conceptually very compatible with this architecture, although we did not set out in this study to perform a dedicated same-site co-recruitment or time-resolved dynamics analysis. There are at least two ways one could envisage doing this: by tiling the same promoter or enhancer with multiple sgRNAs carrying different scaffolds, or by engineering individual sgRNAs that carry more than one aptamer type. In practice, the outcome would likely depend on the geometry of the binding sites, the local chromatin environment, the stoichiometry of the recruited effectors, and whether the effector activities are cooperative or antagonistic.

For example, combining compatible activation or chromatin-opening modules might increase the amplitude, speed, or persistence of target-gene induction. Conversely, juxtaposing activating and repressive modules at the same regulatory region might not simply “cancel out”, but could create intermediate expression states, delayed responses, or context-dependent regulatory memory. We therefore see this as an exciting future application of CIRI: not only to turn genes on or off, but also to quantitatively dissect how regulatory modules cooperate, compete, or shape transcriptional dynamics at endogenous loci.

Could this system incorporate additional types of effectors beyond activators/repressors (e.g. chromatin architectural proteins)?

Yes. This is one of the directions we find most exciting. The CIRI logic is not intrinsically limited to transcriptional activators and repressors. In principle, any effector that can be expressed as a functional RNA-binding protein fusion, localized to the nucleus, and tolerated by the cells could be recruited through the same scaffold-based strategy.

In this study we focused on p65-HSF1-based activation and KRAB-based repression because these provided a clear and stringent proof-of-principle for bidirectional control of cell-fate genes in hiPSCs. However, the same framework could be extended to epigenetic writers, erasers, or readers, including DNA or histone modifiers, chromatin remodelers, or factors that influence enhancer–promoter communication. This would open the possibility of systematically testing how different chromatin marks contribute to gene activation, repression, transcriptional memory, or lineage priming.

Chromatin architectural proteins are also an attractive possibility. In the discussion, we speculate that CIRI-like approaches could eventually connect gene regulation, live imaging, and 3D genome engineering by recruiting modules that reposition loci, modulate looping, or influence nuclear compartmentalization. We would emphasize, however, that each new effector class will need empirical optimization. Effector size, linker configuration, expression level, potential toxicity, chromatin context, and the timing of recruitment can all strongly influence performance. Therefore, we see CIRI as a flexible platform for testing these possibilities rather than as a guarantee that every effector will work without further engineering.

Why was myogenic differentiation chosen as the primary model, and how well does the approach translate to other systems?

We chose myogenic differentiation for both biological and practical reasons. Biologically, MYOD1 is the archetypal master transcription factor for cell-fate conversion and remains one of the best-characterized examples of a single factor capable of initiating a lineage program. This made it an ideal stress test for asking whether endogenous CRISPRa, rather than cDNA overexpression, could drive forward programming from hiPSCs.

Practically, skeletal muscle programming provides clear and quantitative readouts: morphology, activation of myogenic markers, sarcomeric protein organization, the TTN-mEGFP reporter, flow cytometry, bulk RNA-seq, and single-cell RNA-seq. It also offered a useful benchmark against established MYOD1 cDNA-based forward programming systems. Importantly, MYOD1 activation alone produced substantial differentiation but also residual heterogeneity, which made it possible to test whether simultaneous repression of pluripotency factors could canalize the trajectory. That was exactly where CIRI proved most informative.

Regarding transferability, we do not view the platform as muscle-specific. We validated inducible CRISPRa/i activity in hiPSCs and in cardiac organoids, and showed activation of targets associated with different lineages, including MYOD1, ASCL1, and HNF1A. Our preliminary ASCL1 experiments also support the principle that CIRI can be applied to neuronal forward programming. At the same time, the optimal implementation will clearly be lineage-specific. Different cell fates will require different master regulators, guide designs, media conditions, induction windows, and repression strategies. For example, ASCL1 alone is not necessarily the strongest or most complete neurogenic driver in this context; future neuronal applications may benefit from stronger or more combinatorial neurogenic regulators such as NGN2, or from ASCL1 combined with additional factors. Similarly, repressing SOX2 may be counterproductive in early neural contexts, where SOX2 can be part of the desired developmental program. Thus, we see CIRI as a generalizable regulatory framework, but one that should be adapted thoughtfully to each lineage.

What is the genome-wide specificity of the system, and how significant are off-target or secondary transcriptional effects?

We think it is useful to distinguish two related but different issues: the baseline impact of installing the CIRI chassis, and the transcriptional consequences of guide-directed perturbations.

For the baseline chassis, our data are reassuring. Stable expression of OPTtetR and dCas9 from safe-harbor loci did not alter key pluripotency markers, and bulk RNA-seq comparing TetCas hiPSCs with parental cells showed only limited transcriptional differences. We also did not observe enrichment of DNA damage or p53-related signatures, and molecular karyotyping did not reveal newly acquired large-scale copy-number changes. These analyses suggest that the core platform itself is well tolerated in hiPSCs.

For guide-directed perturbations, interpretation is more complex. In forward programming experiments, the desired perturbation is intentionally strong: activating MYOD1 and repressing pluripotency genes is expected to trigger broad downstream transcriptional remodeling. Therefore, many genome-wide changes are not “off-targets” in the CRISPR sense, but secondary or tertiary effects of rewiring the cell-fate network. This is particularly true for master regulators such as MYOD1, where the whole purpose is to initiate a new transcriptional program.

We controlled for this using non-targeting controls, Tet-off controls, targeted molecular readouts, and single-cell profiling to assess whether cells moved into the intended lineage trajectory or into alternative states. The comparison between MYOD1 CRISPRa alone and MYOD1a-OSNi CIRI was especially informative: CIRI reduced mis-differentiated populations and more effectively canalized cells toward a myogenic fate.

That said, we would not claim that we have exhaustively mapped every possible dCas9 binding event or guide-dependent off-target interaction. A more direct future experiment could combine chromatin occupancy profiling of dCas9/effectors with a genetically insulated setting in which MYOD1 protein output is prevented while the same MYOD1-targeting sgRNAs are induced (i.e., by introducing a premature stop codon in MYOD1). This would help separate guide-intrinsic off-target effects from the legitimate downstream consequences of MYOD1-driven fate conversion. Overall, our data suggest that the platform has limited baseline perturbative effects, while the major transcriptional changes observed during programming largely reflect the intended rewiring of cell identity.

How tuneable is the strength of activation or repression for individual targets?

There are several potential levels of tunability in CIRI. The first is guide choice: different sgRNAs can vary substantially in activity depending on their position, the local chromatin environment, and the regulatory architecture of the target locus. The second is guide number and placement. For MYOD1, we showed that combining multiple promoter-targeting guides markedly increased activation compared with a single guide, while maintaining very low baseline expression in the absence of Tet. This provides a practical way to increase activation strength when a single guide is insufficient.

A third level is temporal and dose control through the Tet-inducible sgRNA system. In this study, our main goal was to maximize robust and synchronous induction, so we did not systematically perform Tet dose–response experiments for CIRI. However, based on our previous work with Tet-inducible Pol III systems in hPSCs, and on the tight Tet-dependent gating observed here, we expect that induction strength and duration should be adjustable by changing Tet concentration, timing, and exposure length. This will need to be calibrated for each target and application.

Finally, effector choice provides another axis of tuning. For example, different activators or repressors can differ in potency, persistence, and chromatin dependence. Our own optimization also illustrates an important caveat: a nominally stronger effector is not always better in hiPSCs, because recruitment geometry, expression burden, cell-state compatibility, and locus context all matter.

Therefore, CIRI should be considered tuneable, but not as a simple universal dial. For each target, the achievable dynamic range will depend on sgRNA design, chromatin accessibility, effector activity, transcript stability, and the biology of the gene being regulated. In the current study we prioritized robust induction for forward programming; systematic dose-response and kinetic studies will be an important next step for applications requiring graded rather than maximal perturbation.

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Combinatorial and Inducible CRISPRa/i Enables Canalized hiPSC Forward Programming and Iterative Refinement via Single-Cell Genomics

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Key findings

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