John Lock

Cancer Systems Microscopy Lab, @UNSW. Imaging-based single cell systems biology mapping heterogeneity to understand cancer cell biology.

[John Lock @locusJ]

Our #AI #research @NatureComms will help you use concepts from #multiomics data integration to fuse 4-channel imaging data into unlimitedly multiplexed image datasets to interrogate complex cell biology, like #EMT. https://t.co/cL9APCq064 @UNSW @UNSWMedicine @vafaee_fatemeh

[John Lock @locusJ]

Check out my latest article: Semantic Multiplexing (SemaPlex) for scalable & accessible multiplexed fluorescence imaging https://t.co/ydiQmZ3EXj via @LinkedIn

[John Lock @locusJ]

I've always loved the interface of art & science. @GarvanInstitute are doing this so well. It's such a great way to start a conversation & make the complexity of scientific image data appealing & accessible. Maybe @UNSWMedicine @artdesignunsw to up the ante? Science-Art Collab?

[Garvan Institute of Medical Research @GarvanInstitute]

The Art of Discovery exhibition is now open! 11-22 Aug, 2-5pm @GarvanInstitute. Immerse yourself in a free illuminated art exhibition, or explore the gallery online & vote for your favourite. https://t.co/NNguTHDrVG

[John Lock @locusJ]

@felkohdigital wrote a beautiful popular science piece about how AI is advancing the inference of molecular function from analysis of cellular form, i.e. image-based phenotyping. If you want to explain your #HCS project to a friend, this might help! @unsw https://t.co/HPN210w85Q

[John Lock @locusJ]

@felkohdigital wrote a beautiful popular science piece about how AI is advancing the inference of molecular function from analysis of cellular form, i.e. image-based phenotyping. If you want to explain your #HCS project to a friend, this might help! @unsw https://t.co/HPN210w85Q

[John Lock @locusJ]

Amazing to join the $25M National Breast Cancer Foundation
@NBCF -funded AllClear team, fighting to find & treat dormant cancer cells, the 'seeds of relapse', hiding in bone & causing metastatic relapse in patients. @GarvanInstitute @UNSW @c_chaffer https://t.co/eWUbmbzLRa

[Anne Carpenter, PhD @DrAnneCarpenter]

Have $10M and want to cure some diseases?

It costs ~$1B to invent a new medicine…but $10M buys you Project Encore, to see if ANY existing drug might be repurposed for ~100 diseases that have no treatments.

Contact me - be a hero for desperate patients!
https://t.co/9mKWkkRWYy

[John Lock @locusJ]

@UNSW @unsw_ai @UNSWMedicine

[John Lock @locusJ]

Our #Extensible_Immunofluorescence (ExIF) technique is a bit like #AI doing #origami. From some starting information (raw images = sheets of paper), AI can re-fold many alternatives (virtual images = alternate origami), capturing more #single-cell biology. https://t.co/cx61yTcvrG

[John Lock @locusJ]

Our #Extensible_Immunofluorescence (ExIF) technique is a bit like #AI doing #origami. From some starting information (raw images = sheets of paper), AI can re-fold many alternatives (virtual images = alternate origami), capturing more #single-cell biology. https://t.co/cx61yTcvrG

[John Lock @locusJ]

Not just noise! Single-cell quantitative imaging reveals that cell state in the epithelial-mesenchymal #EMT spectrum pre-determines different magnitudes, kinetics & localisation of EGF-induced phospho-signalling. @felkohdigital @c_chaffer @vafaee_fatemeh https://t.co/JmLQZ3GvVf

[John Lock @locusJ]

New collab with & led by @KrisKilian showing brief capillary constriction drives cancer stem cell traits & tumorgenicity based on PIEZO mechanosensitivity. CTCs get an 'aggressiveness boost' from squeezing through small blood vessels. @UNSW @UNSWMedicine https://t.co/14fqcbzvwW

[John Lock @locusJ]

@osvaldoics @NatureComms @UNSW @UNSWMedicine @vafaee_fatemeh Thanks @osvaldoics! It was a fun paper, and we learned a lot about the nature of the biological information associated with fluorescence imaging data. We've got several follow-up concepts in the works...!!!

[John Lock @locusJ]

Our #AI #research @NatureComms will help you use concepts from #multiomics data integration to fuse 4-channel imaging data into unlimitedly multiplexed image datasets to interrogate complex cell biology, like #EMT. https://t.co/cL9APCq064 @UNSW @UNSWMedicine @vafaee_fatemeh

[John Lock @locusJ]

Our #AI #research @NatureComms will help you use concepts from #multiomics data integration to fuse 4-channel imaging data into unlimitedly multiplexed image datasets to interrogate complex cell biology, like #EMT. https://t.co/cL9APCq064 @UNSW @UNSWMedicine @vafaee_fatemeh

[John Lock @locusJ]

@SamStehbens Consider it considered... The thing I've always liked about this photo @SamStehbens is what I interpret as the awed expression on your face. Other interpretations not considered...

[John Lock @locusJ]

Excited to share Cancer Systems Microscopy lab @UNSWMedicine multiplexed imaging (60+ markers/cell) for #EMT research & #CTC phenotyping from #liquidbiopsy, + extensible immunofluorescence (ExIF) for ♾️ computational multiplexing via routine 4-plex IF. https://t.co/NHli0wA1jy

[ニコンソリューションズ/バイオサイエンス @nikonsolutions]

10/15(火)14時～開催📣
「Nikon Asia Pacific Life Science Webinar」

【演題】Towards Precision Medicine with Systems Microscopy
【演者】Dr. John Lock @locusJ（ニューサウスウェールズ大学）
※本ウェビナーは英語講演です

▽お申込みはこちら
https://t.co/BhxIUIvKkO

#Nikon #顕微鏡

[Greg Bodwin @GregBodwin]

In academia you sometimes have to work 7 days a week, but the freedom to choose which 7 days is unparalleled

[Fabian Theis @fabian_theis]

Interested in applying Prophet for your own functional assay? Check out the manuscript https://t.co/scdgQU3ZJX or grab the model itself: https://t.co/0Seu9qFjRW
Really excited about this new predictive approach for phenotype & drug discovery, check it out!

[Fabian Theis @fabian_theis]

Its flexible encoding allows Prophet to predict genetic perturbations, small molecules & treatment combinations within one model. It scales w data, improving as it learns. Interestingly, learning across phenotypes boosts results, like using IC50 or RNA data to infer morphology.

[Fabian Theis @fabian_theis]

By leveraging transfer learning across various phenotypes, Prophet allows in silico prediction of unseen perturbations in unseen biological contexts to predict which experiments are more likely to succeed - reducing the number to perform by up to 60x in the IC_50 example below.

[Fabian Theis @fabian_theis]

Thrilled to share our „PRedictor Of PHEnoTypes“ model Prophet! Led by @Alejandro__TL & @_yji_, Prophet is a transformer-based model that predicts outcomes for unseen experiments. It aims to understands biology by learning across assays and phenotypes over 4.7M+ experiments.