Colossal Biosciences is growing chickens in a 3D-printed artificial eggshell

The baby chicks were shifting and starting to pip—or trying to hatch. But not from an egg. 

Instead, these chickens were growing inside transparent 3D-printed plastic cups at the Dallas headquarters of Colossal Biosciences.

The biotech company today claimed it has developed a “fully artificial egg” as part of its effort to resurrect extinct avian species, including birds like the dodo and the giant moa.

But “artificial eggshell” would probably be a better description for the invention. It’s an oval-shaped printed lattice, coated inside with a special silicone-based membrane that lets in oxygen, just as a real eggshell does. 

To generate birds, Colossal took recently laid chicken eggs and carefully poured their contents into the artificial shells, where they continued growing. A window on top lets researchers peek inside.  

“To see them all moving around in their artificial eggs was absolutely mind blowing,” says Andrew Pask, the company’s chief biology officer. “You really feel you can grow life outside of the womb.”

Colossal was founded in 2021 with plans to use gene editing and reproductive technology to restore extinct species, including the woolly mammoth. It’s since raised more than $800 million toward what it now terms the “scalable and controllable” creation of animals.

According to Pask, the egg technology could help conserve at-risk bird species. It could also play a role in a project to re-create the extinct giant moa, a flightless 12-foot-tall bird that once lived in New Zealand and laid four-liter eggs, larger than those of any living bird.

But Colossal may be able build one that’s big enough. The company provided a photograph of a prototype 3D-printed egg so large that staff have started to call it the “salad spinner.”

The moa went extinct after canoes carrying the ancestors of the Maori arrived on New Zealand’s South Island about 750 years ago. Archeological sites showcase the birds’ bones alongside stone cutting tools—clear evidence that they were hunted.

To be clear—Colossal isn’t close to re-creating the moa. Before that could happen, scientists would need to study DNA data from old moa bones and insert thousands of genetic changes into the genome of an existing bird, something that’s still technically difficult to do—with or without an artificial egg.

artificial womb for chicken embryos

COLOSSAL BIOSCIENCES

Some scientists also think Colossal is taking too much credit for its artificial eggshell, which it announced in a thundering YouTube video intoning that the company has solved the “impossible question of which came first, the chicken or the egg.”

The video is pure Hollywood—it’s meant to be funny and exciting. But Colossal has a habit of antagonizing scientists by making false and exaggerated claims. Last year, for instance, the company said it had re-created the extinct dire wolf—a claim widely rejected by experts. 

This time, Colossal’s fluffed-up assertion of having created the “first-ever shell-less incubation system” is what’s raising hackles among the small flock of scientists who’ve been working on the technology for years. 

“Clearly an overstatement,” says Katsuya Obara, at the University of Tsukuba in Japan, who in 2024 hatched chickens from beneath transparent plastic film. “The technology here is essentially a modification of existing methods.”

In fact, Obara notes, growing birds in artificial containers goes all the way back to 1998, when another Japanese group managed to do it with quail.

What may be an advance by Colossal is the special membrane, which lets the embryo access more oxygen. Previous systems required scientists to supplement the gas—something that may not have been good for the chicks, as often some of them would fail to hatch. 

The work on the artificial eggshell was carried out in Dallas by Colossal’s exogenous development team, or Exo Dev. That group is also trying to develop artificial wombs for mammals, starting with marsupials.

“We’re looking at every single facet of what’s happening during a mammalian pregnancy to unpack exactly how we then go about recapitulating that,” says Pask.

For that team, an artificial eggshell is a relatively quick and easy technical win. That’s because chickens are already an example of ex utero development. After an egg is laid, a small embryo sitting on top of the yolk starts growing, drawing nutrients from the yolk, the white, and even the shell, which provides calcium. (Colossal says it has to add ground-up calcium to the artificial eggs.)

looking down into the artificial egg shell to see a developing chick embryo and its vascular structure

COLOSSAL BIOSCIENCES

In order to create a moa, Colossal will have to genetically alter another type of bird, changing potentially thousands of DNA letters. But so far, chickens are the only bird species that can be genetically engineered. And that’s via a tricky process of editing stem cells that produce egg and sperm. Scientists have to add or delete DNA letters from these cells and then inject them back into an egg. The resulting bird will carry the genetic changes in its gonads—and then be able to pass them on. 

Pask says Colossal’s idea is that it could modify avian stem cells enough to produce moa-like sperm or eggs. But then you might have the odd situation of a chicken laying an egg with a moa embryo inside it. “You would have chickens making moa egg and moa sperm. But it’s still a chicken egg,” he says.

Helen Sang, a professor emeritus at the Roslin Institute in the United Kingdom, says she’s not sure a moa embryo could survive on the yolk of a chicken egg, given evolutionary differences. “There are significant challenges to overcome to grow an embryo of a different species in artificial eggs,” says Sang.

Just one of those is the huge size discrepancy. The amount of yolk in a chicken egg would hardly be enough to support the much larger moa chick. Yet Pask says that is exactly where the artificial egg will come in handy.

He says it may be possible to use a fine needle to slowly “put 50 yolks together to make that yolk mass much larger.”

“The chicken egg isn’t going to be big enough to support the growth of the moa through to term, to when it would normally hatch, but that’s when you could then take that egg, put it into the artificial egg environment, and then scale it up in size,” he says.

So far, Pask says, the artificial egg is working well for chickens—almost too well. “We hatched 26 chickens and then [our CEO] asked us to put the brakes on. We have too many chickens running around.”

Machine learning-based predictive factor analysis of depression among Chinese adolescents

IntroductionAdolescent depression has emerged as a critical global public health concern, with rising prevalence in China posing severe threats to psychological development and social adaptation. Traditional statistical methods face limitations in capturing complex non-linear relationships and interactions among influencing factors, while machine learning algorithms offer advantages in predictive modeling of mental health disorders.ObjectiveThis study aimed to: (1) compare the performance of seven ML algorithms in classifying low and high depression risk groups among Chinese adolescents; (2) identify key predictive factors from demographic, personality, and PGI-related variables; (3) explore non-linear relationships and interactive effects between critical factors; and (4) explore preliminary threshold values for key factors as potential references for risk identification.MethodsA total of 559 Chinese adolescents completed assessments of demographic characteristics, Big Five personality traits, personal growth initiative, and depression symptoms. Model performance was compared using Friedman tests and Nemenyi post-hoc tests appropriate for correlated cross-validation data. Seven ML algorithms were trained and optimized using 5-fold cross-validation. Feature importance was analyzed via traditional metrics and SHAP values, and SHAP interaction effects were tested using permutation tests. Threshold analysis was conducted using the Youden’s J statistic.ResultsLightGBM outperformed other models with an AUC of 0.834, achieving balanced accuracy, sensitivity, and specificity. Neuroticism emerged as the most robust predictor across all models, followed by proactive change, agreeableness, extraversion, and growth resilience. Demographic factors showed minimal predictive power. SHAP permutation tests confirmed significant interactions between neuroticism and proactive change and between proactive change and agreeableness, whereas no significant interaction was found between neuroticism and agreeableness. Preliminary thresholds were identified for key factors within this sample.ConclusionML algorithms, particularly lightGBM, effectively identify adolescent depression risk, with personality traits and PGI serving as core predictive factors. The findings highlight the value of integrating multi-dimensional variables in depression prediction and provide preliminary references for early intervention. Given the cross-sectional design and lack of external validation, conclusions regarding generalizable cutoffs and causal inference should be made with caution. Targeted strategies focusing on reducing neuroticism and enhancing proactive growth behaviors may mitigate depression vulnerability in Chinese adolescents.

Understanding the workplace needs of autistic adults in Singapore: insights to inform inclusive AI support

IntroductionAutistic adults face persistent challenges in obtaining and sustaining meaningful employment. Despite growing attention to workplace inclusion, research on autistic adults’ employment experiences in non-Western contexts remains scarce. The potential of emerging technologies, such as large language models (LLMs), to support workplace integration is also still largely unexplored, yet off-the-shelf models may reflect neurotypical norms that risk reinforcing masking or overlooking neurodivergent needs. This qualitative, participatory study therefore centered autistic adults’ perspectives to understand workplace experiences and identify potential LLM affordances that could facilitate integration.MethodsTwenty autistic adults with at least 3 months of work experience were recruited in Singapore. Data was collected through semi-structured group discussions. Using reflexive thematic analysis, we identified workplace challenges, needs, and potential support LLMs can provide. We had a multidisciplinary team of autistic and non-autistic researchers, and autistic perspectives actively shaped the design, conduct, and interpretation of the research.ResultsTwo overarching themes emerged: (1) assumed neurotypicality of the workplace, evident in work processes and social participation, and (2) need for workplace inclusivity, supported through both individual accommodations and systemic change, including identity-affirming support, alignment of work design and tools with neurodivergent working styles, empowered access to supports and accommodations, and shared responsibility for workplace integration. Potential LLM functionalities involve supporting executive functioning, encouraging self-reflection, and fostering mutual understanding between autistic employees and their coworkers.DiscussionWorkplace barriers for autistic employees often stem from assumed neurotypical norms rather than individual deficits. Participants reported challenges related to ambiguous work processes, implicit social expectations, and executive functioning demands, which reflect a mismatch between workplace structures and neurodivergent ways of working. Crucially, inclusivity cannot rely solely on individual accommodations; meaningful workplace inclusion requires systemic change. Designing LLM tools that align with neurodivergent working styles can complement systemic inclusivity efforts and empower autistic employees. Implications and future directions are discussed.

Antibody-drug conjugates in breast cancer: Progress and future directions

Newman et al. review the evolving landscape of antibody-drug conjugates (ADCs) in breast cancer, including approved agents, resistance mechanisms, and combinatorial strategies. The authors highlight novel agents, antigen targets, and next-generation platforms, underscoring the need for predictive biomarkers and optimized sequencing strategies to improve patient selection and efficacy.

BDNF insufficiency exacerbates ALS progression

Xu and He et al. provide in vivo evidence that BDNF insufficiency exacerbates the symptoms and pathologies in amyotrophic lateral sclerosis (ALS) patients and mouse models, leading to accelerated demise. They develop an agonistic antibody that boosts BDNF signaling and demonstrates therapeutic benefits in multiple models of ALS mice.

A human iPSC-derived sensory neuron platform for high-throughput discovery of neuroprotectants against chemotherapy-induced peripheral neuropathy

Petrova et al. develop a high-throughput human iPSC-derived sensory neuron platform for drug discovery in chemotherapy-induced peripheral neuropathy. They uncover that inhibition of three members of the STE20 kinase family (MAP4K4/MINK1/TNIK) is neuroprotective against paclitaxel-induced axon damage, with validation in primary human and mouse models.