Channel
@ascanews
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
2,015subscribers
+22 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001504457127 |
|---|---|
| Type | Channel |
| Username | @ascanews |
| Created | Between 1 July 2021 and 28 February 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 16 September 2026 |
| Measurements held | 13 |
| Confirmed unchanged | 1 time, most recently 16 September 2026 |
| On Telegram | t.me/ascanews |
Other / unclassifiable — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 16 September 2026 and assigned it the closest of 31 fixed categories, at 52% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 16 Sept 2026, 02:18 | 2,015 | +1 |
| 12 Sept 2026, 02:41 | 2,014 | +2 |
| 7 Sept 2026, 11:36 | 2,012 | +3 |
| 2 Sept 2026, 17:15 | 2,009 | +4 |
| 30 Aug 2026, 11:45 | 2,005 | +3 |
| 27 Aug 2026, 02:09 | 2,002 | +2 |
| 24 Aug 2026, 12:28 | 2,000 | -1 |
| 20 Aug 2026, 10:57 | 2,001 | -3 |
| 17 Aug 2026, 17:14 | 2,004 | +4 |
| 14 Aug 2026, 22:04 | 2,000 | +6 |
| 7 Aug 2026, 23:48 | 1,994 | +1 |
| 7 Aug 2026, 00:01 | 1,993 | no change |
| 6 Aug 2026, 23:48 | 1,993 | first reading |
20 posts held, back to 15 June 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pages of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 13 July 2026. An engagement rate over an empty window would be a number about nothing.
176 reactions across 19 posts, in 12 distinct kinds. The most used accounts for 23.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 41 | 23.3% | |
| 🥰 | 38 | 21.6% | |
| ❤ | 34 | 19.3% | |
| 👍 | 19 | 10.8% | |
| 🤗 | 13 | 7.39% | |
| 👏 | 11 | 6.25% | |
| 🤝 | 9 | 5.11% | |
| ❤🔥 | 5 | 2.84% | |
| ⚡ | 3 | 1.70% | |
| 🆒 | 1 | 0.568% | |
| 💘 | 1 | 0.568% | |
| 😁 | 1 | 0.568% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 19 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 176 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 15 June 2026 to 13 July 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Single Cell News pinned «Dear colleagues! ⚡️ We’re taking a short summer breather to recharge. Hope your summer is filled with sunshine, adventure, and plenty of fun. We’ll be back in September - can’t wait to catch up then! 🏖️ All the best, ASCA Team #ASCAinfo»
Dear colleagues! ⚡️ We’re taking a short summer breather to recharge. Hope your summer is filled with sunshine, adventure, and plenty of fun. We’ll be back in September - can’t wait to catch up then! 🏖️ All the best, ASCA Team #ASCAinfo
🤗13🤝9🥰8👍4
A comprehensive epidermal map from a poplar single-cell shoot atlas reveals new trichome-specific genes Anita Giabardo ... C. Robin Buell presented a single-cell RNA-seq atlas of the poplar 717 shoot, including apical meristem, primary and secondary stems, and three stages of leaf development. Key points 🔹159,000 cells, 40 transcriptionally distinct clusters, and 7 major cell types 🔹Focus on the epidermis which con…
🥰2👏1
HLiCA: An integrated cell atlas of the healthy human liver Rachel Edgar ... Gary Bader presented the Human Liver Cell Atlas (HLiCA), an integrated reference of non-disease liver cells assembled from eight datasets across six research centers, encompassing more than 525,000 cells from 110 donors. Key points 🔸 Expert-curated cell annotations 🔸 Classifying cells into six lineages and 47 distinct types 🔸 Evaluating the…
❤2👍2🥰2
Pubertal maturation and chemotherapy-associated disruption of the pediatric ovary revealed by multimodal single-cell profiling Loren Méar … Pauliina Damdimopoulou analyzed ovarian tissue from 16 patients undergoing fertility preservation (aged 1-16 years) and 11 adult controls (aged 22-32 years) using scRNA-seq, spatial transcriptomics, and multiplex immunostaining. Key points ✔️ Extensive stromal remodeling across…
❤3⚡1🥰1
⚡Dear Colleagues, 📍 Don't miss the registration for the ASCA CONGRESS 2026, which is open until July 24. The program includes 11 sections with talks by key researchers and technology developers in the field of single-cell and spatial analysis. 📍 Online access is also available. ➡️ More details: https://ascanet.org/events/asca-congress-2026/ Sincerely, Organizing Committee
🥰6🔥3
Four-dimensional molecular mapping from a spatial snapshot reveals the dynamics of hair follicle organogenesis Soichiro Asami ... Reza Kalhor developed 3D DNase-Enhanced Expression Profiling (3DEEP), a tissue-clearing approach that removes genomic DNA to extend spatial transcriptomic profiling hundreds of microns into intact tissues. Highlights 🔸 Spatial pseudotiming reconstructs hair follicle organogenesis in spac…
❤3🥰3⚡1
SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types Carlos Anerillas ... Myriam Gorospe profiled the transcriptomes and proteomes in 14 different primary human cell types undergoing over 30 senescence paradigms to create a senescence catalog “SenCat”. Key points 🔷 Senescent cells lack universal markers but share metabolic and damage-response pathways implic…
❤3🥰2🔥1
scEPS integrates genetic and single-cell disease atlas data to provide granular mechanistic insights into complex human diseases Luli Zou ... Huwenbo Shi developed scEPS, an integrative approach for identifying disease-associated cell neighborhoods, where the expression of disease GWAS genes explains more variance in the disease phenotype than randomly selected mean expression matched control genes. Key points ⚡️Ca…
👍3🥰2⚡1
Spatially resolved profiling of extracellular vesicles in tissues with Spatial-EV-seq Qianxi Wen ... Chaoyong Yang presented Spatial-EV-seq, a method for in situ spatial profiling of extracellular vesicles (EVs) within their native microenvironmental context. Key points ☑️ An antibody-engineered capture interface to preserve EVs’ spatial distribution ☑️ Rolling circle amplification with EV surface-binding aptamers,…
🔥2🥰2🆒1
AI-driven discovery of GPNMB CAR T cells as a multi-cancer therapy Daniel Baker ... Carl June developed an artificial intelligence-driven approach for CAR T cell target discovery by integrating single-cell RNA sequencing datasets from human skin cancer and healthy tissue. Highlights 🔸 Large language models used with biological data nominate tractable CAR T cell targets 🔸 GPNMB is expressed on the surface across a b…
🔥2❤1👏1
The Human Pancreas Cell Atlas defines a healthy reference framework for disease contextualization and translational benchmarking Shrey Parikh ... Fabian Theis presented the Human Pancreas Cell Atlas, a reference atlas of the healthy human pancreas integrating 815,126 single-cell and single-nucleus transcriptomes from 109 donors. Key points ✔️ 94 cell types and transcriptional states spanning endocrine, exocrine, i…
❤3🥰2
Showing the 12 most recent of 20 posts we hold for @ascanews. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 5 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 16 September 2026 — this entry's latest reading, not the date you are reading this.
“Single Cell News” (@ascanews), 2,015 subscribers as measured 16 September 2026. Telegram Register, tgregister.com/channel/ascanews.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.